This is not a traditional analytics role. On this team, data scientists do the core statistical and modeling work, but they also build real tools and products: evaluation explorers, operator workflows, decision-support systems, experimentation surfaces, and customer-specific AI/data applications that get used in production.

The right candidate is strong in first-principles problem solving, rigorous measurement, and technical execution. They know how to define metrics, design experiments, diagnose failures, and build systems that people actually use. They are also comfortable using modern AI-assisted development tools to prototype and iterate quickly without sacrificing reliability, observability, or judgment. Python and SQL matter in this role, but as execution fluency in service of building better products and making better decisions.

Responsibilities: Partner directly with enterprise customers to understand workflows, operational pain points, constraints, and success criteria Turn ambiguous business and product problems into measurable solutions with clear metrics, technical designs, and deployment plans Design and build internal and customer-facing data products, including evaluation tools, workflow applications, decision-support systems, and thin product layers on top of data/ML systems Build end-to-end solutions across data ingestion, transformation, experimentation, statistical modeling, deployment, monitoring, and iteration Design evaluation frameworks, benchmarks, and feedback loops for ML/LLM systems, human-in-the-loop workflows, and model-assisted operations Apply rigorous statistical thinking to experimentation, causal inference, metric design, forecasting, segmentation, diagnostics, and performance measurement Use AI-assisted development workflows to accelerate prototyping and product iteration, while maintaining strong engineering discipline Diagnose failure modes across data quality, model behavior, retrieval, workflow design, and user experience, and drive fixes into production Act as the voice of the customer to Product, Engineering, and Data Science, using field learnings to shape roadmap and platform capabilities

Requirements: 5+ years of experience in data science, machine learning, quantitative engineering, or another highly analytical technical role Proven track record of shipping data, ML, or AI systems that delivered measurable business or product impact Exceptional ability to structure ambiguous problems, define the right success metrics, and translate them into executable technical plans Strong foundation in statistics, experimentation, causal reasoning, and measurement Experience building tools or products, not just analyses , for example internal workflow tools, evaluation systems, operator-facing products, experimentation platforms, or customer-specific applications Hands-on fluency in Python, SQL, and modern data/AI tooling; able to inspect data, prototype quickly, debug deeply, and productionize solutions that work Comfort using AI-assisted coding and development workflows to move from idea to usable product quickly Strong communication and stakeholder management skills; able to work effectively with customers, engineers, product teams, and executives High ownership and bias toward shipping in fast-moving environments with incomplete information

Preferred qualifications: Experience in a forward deployed, solutions, consulting, or other client-facing technical role Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign

What success looks like: Success in this role means taking a messy, high-stakes customer problem and turning it into a deployed system that is actually used. Sometimes that system is a model. Sometimes it is an evaluation framework. Sometimes it is an operator-facing tool or a lightweight data product that changes how decisions get made. In all cases, success is defined by measurable impact, rigorous evaluation, and reliable execution.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Salary Range: $167,200-$209,000 USD

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You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimising the platform to enable our next generation LLM training, inference and data curation.

Key responsibilities include:

• Building, profiling and optimising our training and inference framework.
• Collaborating with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.
• Researching and integrating state-of-the-art technologies to optimise our ML system.

The ideal candidate will have:

• Passionate about system optimisation.
• Experience with multi-node LLM training and inference.
• Experience with developing large-scale distributed ML systems.
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash attention, etc.

Nice to haves include demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

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As a Staff Security Engineer, you will design and implement scalable triage, prioritization, and remediation-tracking systems across application, infrastructure, and hardware domains. You will set technical standards, drive high-impact initiatives, and mentor engineers through technical leadership, while partnering with leadership on priorities and execution risks.

Key Responsibilities:

• Lead high-complexity VM technical initiatives and deliver architecture decisions for assigned program areas
• Design and build scalable triage automation, including integrations, decision logic, and production hardening
• Implement end-to-end workflow components from assessment and detection to ticket routing and remediation tracking
• Provide deep technical leadership on hardware-adjacent vulnerabilities (GPU firmware, DPU firmware/BlueField, and BMC surfaces)
• Act as senior technical responder for embargoed disclosures and zero-day events, coordinating with owner teams that deploy fixes
• Improve prioritization logic, severity models, and exception workflows through code, design reviews, and technical proposals
• Produce actionable technical metrics and risk insights for leadership consumption
• Lead root-cause analysis for high-impact vulnerability incidents and implement durable technical improvements
• Mentor IC3/IC4/IC5 engineers through design guidance, code review, and incident coaching
• Partner with security, engineering, and operational stakeholders to improve workflow reliability and accelerate remediation outcomes

Requirements:

• 9+ years of relevant experience with demonstrated strategic impact in vulnerability management, application security, platform security, or cloud security engineering
• Proven track record building and scaling security automation (SOAR workflows, AI/ML systems, detection pipelines) in production environments
• Deep subject matter expertise with vulnerability management best practices: CVSS, EPSS, CISA KEV, threat intelligence integration, and risk-based prioritization frameworks
• Excellent development background with strong coding skills in Python, Go, or similar languages for building scalable, production-grade security systems
• Significant experience with modern vulnerability management tooling (for example Wiz, Semgrep, Rapid7, Tenable, or equivalent)
• Experience with specialized infrastructure: GPU/DPU environments, firmware security, hardware vulnerabilities, or high-performance computing
• Demonstrated track record mentoring engineers across levels and driving cross-functional technical initiatives at organizational scale
• Strong business acumen and understanding of how security decisions impact engineering velocity, customer trust, and business outcomes

Preferred Qualifications:

• Practical experience building AI/ML-powered security systems (LLM integration, automated decision-making, human-in-the-loop validation) in production
• Experience managing hardware vendor security partnerships (embargoed disclosures and pre-release collaboration)
• Production experience with security automation platforms such as TINES and serverless frameworks (AWS Lambda, GCP Cloud Functions)
• Strong DevOps, DevSecOps, or SRE background with deep experience in AWS/GCP/Azure cloud services and Infrastructure as Code (Terraform, CloudFormation)
• Deep understanding of Kubernetes security (container scanning, admission controllers, supply chain security, runtime protection)
• Experience leading security programs through rapid hypergrowth (10x+ infrastructure scaling) in startup or cloud-native environments
• Practical experience managing vulnerabilities within a FedRAMP-certified environment or similar regulatory frameworks

Salary and Benefits: The base salary range for this role is $188,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

Work Environment:

While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. New hires will be invited to attend onboarding at one of our hubs within their first month. Teams also gather quarterly to support collaboration.

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Scale's ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality.

At Scale, we're uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale's ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.

Responsibilities:

• Build, profile and optimize our training and inference framework
• Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation
• Research and integrate state-of-the-art technologies to optimize our ML system

Ideal Candidate:

• Strong excitement about system optimization
• Experience with multi-node LLM training and inference
• Experience with developing large-scale distributed ML systems
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
• Strong written and verbal communication skills and the ability to operate in a cross functional team environment

Nice to Have:

• Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Compensation Packages:

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please note that our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

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This role focuses on pushing the frontier of reward modeling for large language models. You'll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking. You'll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety.

We're looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You'll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources. Your work will directly advance the science of how we train AI systems to be both highly capable and safe.

Responsibilities:

• Lead research on novel reward model architectures and training approaches for RLHF
• Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability
• Research techniques to detect, characterize, and mitigate reward hacking and specification gaming
• Design experiments to understand reward model generalization, robustness, and failure modes
• Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines
• Contribute to research publications, blog posts, and internal documentation
• Mentor other researchers and help build institutional knowledge around reward modeling

You may be a good fit if you

• Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning
• Have experience training and evaluating reward models for large language models
• Are comfortable designing and running large-scale experiments with significant computational resources
• Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor
• Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences
• Care deeply about building AI systems that are both highly capable and safe

Strong candidates may also

• Have published research on reward modeling, preference learning, or RLHF
• Have experience with LLM-as-judge approaches, including calibration and reliability challenges
• Have worked on reward hacking, specification gaming, or related robustness problems
• Have experience with constitutional AI, debate, or other scalable oversight approaches
• Have contributed to production ML systems at scale
• Have familiarity with interpretability techniques as applied to understanding reward model behavior

The annual compensation range for this role is $350,000-$500,000 USD.

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Our team is responsible for adopting Generative AI technologies to enable an intelligent, scalable, and exceptional service experience. We work closely with product, design, and other engineering counterparts to design and build efficient AI solutions for Airbnb's CS products.

As a Staff Machine Learning Engineer, you will have the opportunity to shape the direction of our AI initiatives and work on cutting-edge projects that transform Airbnb's customer service. You will be responsible for envisioning, championing, and supporting the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems.

Requirements:

• PhD/Master's degree in Computer Science or equivalent experience
• 6/9+ years of ML engineering experience with ownership responsibility over large-scale software systems
• Background in the design and development of AI and ML systems and services
• Experience with LLM driven chatbot and Agentic AI products is a plus
• Excellent communication skills and the ability to work well within a team and with teams across the engineering, product & design organizations

Our Commitment to Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services, and solutions. All qualified individuals are encouraged to apply.

How We'll Take Care of You:

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range: $204,000-$255,000 USD

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This is a unique opportunity to shape how frontier AI models handle dual-use cybersecurity knowledge,balancing the tremendous potential of AI to advance legitimate security research and defensive capabilities while preventing misuse by malicious actors.

In this role, you will lead and grow a team of technical specialists focused on cyber threat modeling and evaluation frameworks. You will serve as the primary domain expert on cyber harms, advising cross-functional teams on threat landscapes and mitigation strategies.

You will collaborate closely with internal and external threat modeling experts to develop training data for safety systems, and with ML engineers to train these systems, optimizing for both robustness against adversarial attacks and low false-positive rates for legitimate security researchers.

You will also analyze safety system performance in traffic, identifying gaps and proposing improvements. You will conduct regular reviews of existing policies and enforcement systems to identify and address gaps and ambiguities related to cybersecurity risks.

You will develop rigorous stress-testing of safeguards against evolving cyber threats and product surfaces. You will partner with Research, Product, Policy, Security Team, and Frontier Red Team to ensure cybersecurity safety is embedded throughout the model development lifecycle.

You will translate cybersecurity domain knowledge into actionable safety requirements and clearly articulated policies. You will contribute to external communications, including model cards, blog posts, and policy documents related to cybersecurity safety.

You will monitor emerging technologies and threat landscapes for their potential to contribute to new risks and mitigation strategies, and strategically address these.

You will mentor and develop team members, fostering a culture of technical excellence and responsible AI development.

To be successful in this role, you will need to have:

• An M.S. or PhD in Computer Science, Cybersecurity, or a related technical field, OR equivalent professional experience in offensive or defensive cybersecurity
• 5+ years of hands-on experience in cybersecurity, with deep expertise in areas such as vulnerability research, exploit development, network security, malware analysis, or penetration testing
• 2+ years of experience managing technical teams or leading complex technical projects with multiple stakeholders
• Experience in scientific computing and data analysis, with proficiency in programming (Python preferred)
• Deep expertise in modern cybersecurity, including both offensive techniques (vulnerability research, exploit development, penetration testing, malware analysis) and defensive measures (detection, monitoring, incident response)
• Demonstrated ability to create threat models and translate technical cyber risks into policy frameworks
• Familiarity with responsible disclosure practices, vulnerability coordination, and cybersecurity frameworks (e.g., MITRE ATT&CK, NIST Cybersecurity Framework, CWE/CVE systems)
• Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders
• Experience developing policies or guidelines at scale, balancing safety concerns with enabling legitimate use cases
• A passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies
• Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve
• Track record of translating specialized technical knowledge into actionable safety policies or enforcement guidelines

Preferred qualifications include:

• Background in AI/ML systems, particularly experience with large language models
• Experience developing ML-based security systems or adversarial ML research
• Experience working with defense, intelligence, or security organizations (e.g., NSA, CISA, national labs, security contractors)
• Published security research, disclosed vulnerabilities, or participated in bug bounty programs
• Understanding of Trust & Safety operations and content moderation at scale
• Certifications such as OSCP, OSCE, GXPN, or equivalent demonstrating technical depth
• Understanding of dual-use security research concerns and ethical considerations in AI safety

The annual compensation range for this role is $320,000-$405,000 USD.

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Required experience includes 10+ years of software engineering experience, 5+ years managing engineering managers, and leading engineering for a consumer or marketplace product where search, matching, ranking, or personalization was core to the business. You should also have scaled an engineering org through a high-growth phase (25+ to 50+) while maintaining velocity and quality, and be technically strong enough to make sound architecture calls on ranking/ML systems, marketplace infrastructure, and consumer-facing surfaces.

Nice-to-have experience includes healthcare experience or other regulated industries where data sensitivity and clinical consequences raise the stakes, experience with marketplace dynamics (supply/demand balancing, multi-sided incentive design), experience building LLM-based product features (conversational interfaces, intelligent triage, AI-assisted workflows), and experience rethinking team structure or hiring profiles in response to AI productivity gains.

Our stack includes Python (Django/FastAPI), TypeScript/React, Elasticsearch, PostgreSQL, Redis, dbt, Snowflake, Temporal, and custom ML models. Everything runs on AWS.

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About Anthropic

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.

About the Role

Safeguards Engineering builds and operates the infrastructure that keeps Anthropic's AI systems safe in production , the classifiers, detection pipelines, evaluation platforms, and monitoring systems that sit between our models and the real world. That infrastructure needs to be not just correct, but reliable: when a safety-critical pipeline goes down or degrades, the consequences can be serious, and they can be invisible until someone looks closely.

As a Technical Program Manager for Safeguards Infrastructure and Evals, you'll own the operational health and forward momentum of this stack. Your primary responsibility is driving reliability , owning the incident-response and post-mortem process, ensuring SLOs are defined and met in partnership with various teams, and making sure that when things go wrong, the right people know, the right actions get taken, and those actions actually get closed out.

Alongside that ongoing operational rhythm, you'll coordinate the larger platform investments: migrations, eval-platform improvements, and the cross-team dependencies that connect them. This role sits at the intersection of operations and program management. It requires genuine technical depth , you need to understand how these systems work well enough to triage effectively, judge what's actually safety-critical versus what can wait, and have informed conversations with the engineers building and maintaining them. But the core of the job is keeping the machine running well and the work moving.

What You'll Do:

• Own the Safeguards Engineering ops review
• Drive the recurring cadence that keeps the team informed and coordinated: surfacing recent incidents and failures, bringing visibility to reliability trends, and making sure the right people are in the room when decisions need to be made.
• Drive incident tracking and post-mortem execution
• Establish and maintain SLOs with partner teams
• Maintain runbook quality and incident-ownership clarity
• Drive platform migrations and infrastructure projects
• Coordinate evals platform improvements

You might be a good fit if you:

• Have solid technical program management experience, particularly in operational or infrastructure-heavy environments , you're comfortable owning a mix of ongoing operational cadences and discrete project work simultaneously.
• Understand how production ML systems work well enough to triage incidents intelligently and have substantive conversations with engineers about what's going wrong and why , you don't need to write the code, but you need to follow the technical thread.
• Are energized by closing loops. Post-mortem action items that never get done, SLOs that no one checks, runbooks that go stale , these things bother you, and you know how to build the processes and follow-ups that fix them.
• Can work effectively across team boundaries , comfortable coordinating with partner teams (like Inference) where you don't have direct authority, and skilled at keeping shared work moving through influence and clear communication.
• Thrive in environments where the work shifts between 'keep the lights on' and 'build something new' , and can context-switch between incident follow-ups and longer-horizon platform projects without dropping either.
• Have experience with or strong interest in AI safety , you understand why the reliability of a safety-critical pipeline is a different kind of problem than the reliability of a product feature, and that distinction motivates you.

Strong candidates may also:

• Have experience with SRE practices, incident management frameworks, or on-call operations at scale.
• Have worked on or with evaluation infrastructure for ML systems , understanding how evals get designed, run, and interpreted.
• Have experience driving infrastructure migrations in complex, multi-team environments , particularly where the migration touches operational systems that can't go offline.
• Be familiar with monitoring and alerting tooling (PagerDuty, Datadog, or equivalents) and the operational culture around them.

Deadline to apply: None, applications will be received on a rolling basis.

The annual compensation range for this role is listed below. For sales roles, the range provided is the role's On Target Earnings ('OTE') range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $290,000-$365,000 USD

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At Coinbase, our mission is to increase economic freedom in the world.

We're seeking a Senior Machine Learning Platform Engineer to join our Machine Learning Platform team. The team builds the foundational components for feature engineering and training/serving ML models at Coinbase. Our platform is used to combat fraud, personalize user experiences, and to analyze blockchains.

As a Senior Machine Learning Platform Engineer, you will:

Form a deep understanding of our Machine Learning Engineers’ needs and our current capabilities and gaps. Mentor our talented junior engineers on how to build high quality software, and take their skills to the next level. Continually raise our engineering standards to maintain high-availability and low-latency for our ML inference infrastructure that runs both predictive ML models and LLMs. Optimize low latency streaming pipelines to give our ML models the freshest and highest quality data. Evangelize state-of-the-art practices on building high-performance distributed training jobs that process large volumes of data. Build tooling to observe the quality of data going into our models and to detect degradations impacting model performance.

What we look for in you:

5+ yrs of industry experience as a Software Engineer. Strong understanding of distributed systems. Lead by example through high quality code and excellent communication skills. Great sense of design, and can bring clarity to complex technical requirements. Treat other engineers as a customer, and have an obsessive focus on delivering them a seamless experience. Mastery of the fundamentals, such that you can quickly jump between many varied technologies and still operate at a high level. Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs and drive measurable improvements in efficiency, cost, and quality.

Nice to haves:

Experience building ML models and working with ML systems. Experience working on a platform team, and building developer tooling. Experience with the technologies we use (Python, Golang, Ray, Tecton, Spark, Airflow, Databricks, Snowflake, and DynamoDB).

Job ID: P75535

Pay Transparency Notice: Depending on your work location, the target annual base salary for this position can range as detailed below. Total compensation may also include equity and bonus eligibility and benefits (including medical, dental, vision and 401(k)). Annual base salary range (excluding equity and bonus): $186,065-$225,000 USD

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Key responsibilities include:

• Drive strategy that fields safe and secure performant agents on embedded warfighting compute.
• Manage a cohort of cleared machine learning engineers, researchers, and product managers.
• Provide deep technical leadership to team on design, development, and deployment of advanced AI solutions.
• Leverage and shape the world's largest defense robotics data set to develop cutting-edge AI capabilities.
• Coordinate with internal testing and evaluation team(s) to design specialized evaluation benchmarks for defense use-cases.
• Partner with US-based Frontier AI Labs to revolutionize the employment of autonomy in embedded hardware.

Required qualifications include:

• Management experience leading and growing AI/ML teams.
• Experience deploying AI-technologies into classified environments (e.g., disconnected, air-gapped, etc.).
• Expertise in deep learning techniques and the latest generative AI technologies (Agents, Vision-Language Action models, etc.).
• Strong programming skills.
• Eligible to obtain and maintain an active U.S. Top Secret SCI security clearance.

Preferred qualifications include:

• Experience with edge-deployed ML systems.
• Prior work in Defense Tech and/or Start Ups.
• Active U.S. Top Secret SCI security clearance.

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Safeguards Engineering builds and operates the infrastructure that keeps Anthropic's AI systems safe in production. As a Technical Program Manager for Safeguards Infrastructure and Evals, you'll own the operational health and forward momentum of this stack.

Your primary responsibility is driving reliability , owning the incident-response and post-mortem process, ensuring SLOs are defined and met in partnership with various teams, and making sure that when things go wrong, the right people know, the right actions get taken, and those actions actually get closed out.

Alongside that ongoing operational rhythm, you'll coordinate the larger platform investments: migrations, eval-platform improvements, and the cross-team dependencies that connect them.

This role sits at the intersection of operations and program management. It requires genuine technical depth , you need to understand how these systems work well enough to triage effectively, judge what's actually safety-critical versus what can wait, and have informed conversations with the engineers building and maintaining them.

But the core of the job is keeping the machine running well and the work moving.

Responsibilities

• Own the Safeguards Engineering ops review
• Drive the recurring cadence that keeps the team informed and coordinated: surfacing recent incidents and failures, bringing visibility to reliability trends, and making sure the right people are in the room when decisions need to be made.
• Drive incident tracking and post-mortem execution
• Establish and maintain SLOs with partner teams
• Maintain runbook quality and incident-ownership clarity
• Drive platform migrations and infrastructure projects
• Coordinate evals platform improvements

Requirements

• Solid technical program management experience, particularly in operational or infrastructure-heavy environments
• Understanding of how production ML systems work well enough to triage incidents intelligently and have substantive conversations with engineers about what's going wrong and why
• Ability to work effectively across team boundaries
• Experience with or strong interest in AI safety

Nice to Have

• Experience with SRE practices, incident management frameworks, or on-call operations at scale
• Familiarity with monitoring and alerting tooling (PagerDuty, Datadog, or equivalents)
• Experience driving infrastructure migrations in complex, multi-team environments

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This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You'll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. You'll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.

Responsibilities

• Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.
• Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.
• Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.
• Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.
• Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.
• Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.
• Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.

Preferred qualifications:

• Experience training or supporting large-scale language models with tens of billions of parameters or more.
• Track record of improving research productivity through infrastructure design or process improvements.
• Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.
• Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.
• Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).
• Contributions to open-source GPU, ML systems, or compiler optimization projects.
• Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.

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Strong candidates will have a track record of solving large-scale systems problems and low-level optimization. They should have significant experience optimizing ML systems for TPUs, GPUs, or other accelerators, and be results-oriented with a bias towards flexibility and impact.

Responsibilities:

• Identify and address performance issues across multiple ML systems
• Design and optimize kernels for the TPU
• Provide feedback to researchers on model changes and their impact on performance

Requirements:

• Bachelor's degree or equivalent combination of education, training, and/or experience
• Relevant field of study
• Years of experience required will correlate with the internal job level requirements for the position

Benefits:

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues

Note: This job description is a rewritten version of the original ad, focusing on the key responsibilities, requirements, and benefits.

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The Account Abuse team at Anthropic is tasked with ensuring the company's computing capacity is allocated fairly, minimizing resources available to bad actors and preventing them from coming back. As a software engineer on this team, you will build systems that gather and analyze signals at scale, balancing tradeoffs and coordinating closely with stakeholder teams throughout the company.

Responsibilities

• Think and respond quickly in a rapidly-changing greenfield environment
• Jump into other teams' code to identify key points to gather signals or introduce interventions with minimal impact on their systems' stability, complexity, or overall architecture
• Integrate with third-party data-enrichment vendors
• Create monitoring dashboards, alerts, and internal admin UX
• Work closely with data scientists to maintain situational awareness of current usage patterns and trends, and with the Policy & Enforcement team to maximize the impact of their human-review availability
• Build robust and reliable multi-layered defenses
• Lead root cause analyses and deep-dive investigations into account activity to identify abuse patterns, uncover emerging attack vectors, and inform both immediate enforcement actions and longer-term systemic defenses

Requirements

• Bachelor's degree in Computer Science, Software Engineering or comparable experience
• 5-10+ years of experience in a software engineering position, preferably with a focus on integrity, spam, fraud, or abuse detection
• Proficiency in Python, SQL, and data analysis tools
• Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders

Preferred qualifications

• Experience building trust and safety mechanisms for and using AI/ML systems, such as fraud-detection models or security monitoring tools or the infrastructure to support these systems at scale
• Experience working closely with operational teams to build custom internal tooling

Annual compensation range

$320,000-$405,000 USD

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This role combines collaboration with hand-on work. You’ll partner with tech leads to set the technical direction for your team and own its execution. You should also be ready to go deep on system design and contribute directly when needed.

Responsibilities:

• Lead and grow a team of senior and staff-level engineers, setting clear expectations and maintaining a high bar for execution.

• Own architecture, system design, and long-term technical direction for your team's systems, with emphasis on reliability and performance.

• Contribute directly to design reviews, prototyping, and debugging critical issues.

• Partner with researchers and product teams to define roadmaps and prioritize work.

• Hire and close senior engineering talent. Mentor engineers into technical leaders.

Skills and Qualifications:

• Minimum qualifications:

• Bachelor’s degree or equivalent industry experience in computer science, engineering, or similar.

• 8+ years of experience building and scaling production systems, including system design and distributed systems.

• 3+ years of engineering management experience in high-growth environments.

• Preferred qualifications , we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• Experience managing teams of senior or staff-level engineers.

• Background in infrastructure, systems engineering, or developer productivity.

• Familiarity with AI/ML systems, data infrastructure, or high-performance computing.

• Track record of building or contributing to widely used systems, platforms, or tools.

Logistics:

• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $400,000 - $500,000 USD.

• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

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Responsibilities:

• Work on an empirical project aligned with our research priorities
• Produce a public output such as a paper submission
• Collaborate with our research team

Requirements:

• Strong technical background in computer science, mathematics, or physics
• Fluent in Python programming
• Available to work full-time on the Fellows program

Preferred qualifications include experience in areas of research or engineering related to reinforcement learning, and strong software engineering skills with experience building complex ML systems.

As a fellow, you will have access to a shared workspace in London, direct mentorship from our researchers, and a weekly stipend of $3,850 USD / £2,310 GBP / $4,300 CAD. You will also have the opportunity to collaborate with our research team and contribute to the development of our AI systems.

Logistics:

• To participate in the Fellows program, you must have work authorization in the UK and be located in the UK during the program.
• We have designated shared workspaces in London where fellows will work from and mentors will visit.
• We are not currently able to sponsor visas for fellows.

Please note that we do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit for full-time roles at Anthropic.

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We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).

We’re hiring an AI Solutions Engineer, Post-Sales to help customers solve real-world problems by enabling them to implement and scale ML pipelines and agentic workflows using Weights & Biases. In this role, you’ll collaborate with engineering teams to ensure smooth onboarding and adoption, act as a trusted advisor on best practices, and represent the voice of the customer internally.

You will partner directly with leading AI teams to optimize workflows, share technical expertise, and influence our product roadmap based on real-world customer feedback.

This is an ideal opportunity for ML practitioners who are customer-focused and eager to work with top AI companies globally.

Responsibilities

• Collaborate with engineering teams to ensure smooth onboarding and adoption of Weights & Biases
• Act as a trusted advisor on best practices for implementing and scaling ML pipelines and agentic workflows
• Represent the voice of the customer internally and influence our product roadmap based on real-world customer feedback
• Partner directly with leading AI teams to optimize workflows and share technical expertise

Requirements

• 3–5 years of relevant experience in a similar role
• Strong programming proficiency in Python
• Hands-on experience enabling production-grade ML systems, with a focus on training and inference pipelines, experiment tracking, deployment patterns, and observability using deep learning frameworks (TensorFlow/Keras, PyTorch/PyTorch Lightning) and MLOps tooling (e.g. Airflow, Kubeflow, Ray, TensorRT)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Experience with GenAI/LLMs and related tools (e.g. LangChain/LangGraph, HuggingFace Transformers, Pinecone, Weaviate)
• Strong experience with Linux/Unix
• Excellent communication and presentation skills, both written and verbal
• Ability to break down and solve complex problems through customer consultation and execution

Preferred

• Background in robotics
• TypeScript experience
• Proficiency with Fastai, scikit-learn, XGBoost, or LightGBM
• Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes
• Familiarity with data pipeline tools

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More than 70,000 companies around the globe, including WeWork, Asana, NASDAQ, AAA Insurance, COMPASS Realty, Uber, Randstad, and Tractor Supply, rely on Dialpad to build stronger customer connections using real-time, AI-driven insights.

We’re now leading the shift to Agentic AI: intelligent agents that don’t just analyse conversations but take action by automating workflows, resolving customer issues, and accelerating revenue in real time. Our DAART initiative (Dialpad Agentic AI in Real Time) is redefining what a communications platform can do.

Visit dialpad.com to learn more.

Being a Dialer --------------- AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more.

We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves.

We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level.

We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic.

Your role -------- As a Sr. SDET in Agentic QA, you will own the test automation and quality frameworks that support Dialpad’s AI Voice Agent services.

You will develop automated tests for end-to-end product experiences, from frontend UI to backend services to APIs to audio/text interactions.

You will test orchestration flows, agent configuration experiences, and guardian safeguards to create robust automated coverage for functionality, performance, reliability, UX, and more.

In this role, you will develop substantial amounts of automated test infrastructure and partner deeply with the development team to make our fast-growing AI platform more testable, more stable, and more delightful for customers.

This position is based at one of Dialpad’s Canadian offices and reports to a QA Eng Manager in the United States.

What you’ll do ------------

• Own end-to-end quality for agentic features and workflows, including strategy, development, execution, and release qualification.
• Design and build automation tooling and frameworks for AI/LLM-driven systems, including prompt flows, agent orchestration, and tool integrations.
• Develop and maintain evaluation frameworks (evals) to measure response quality, accuracy, and hallucination rates.
• Drive automation coverage (80%+ for critical AI workflows) using deterministic + probabilistic validation approaches.
• Integrate AI quality checks into CI/CD pipelines with fast feedback cycles (
• Build tooling for LLM observability and debugging, including prompt tracing and response analysis.
• Partner with Applied AI teams on prompt engineering, model selection, and evaluation strategies.
• Design and execute performance and load tests for AI services (latency, throughput, cost efficiency).
• Identify and mitigate risks related to hallucinations, bias, safety, and edge cases.
• Define and track AI quality KPIs (task success rates, precision/recall, latency, etc.).
• Participate in design and architecture reviews to ensure systems are testable, observable, and resilient.
• Mentor engineers and contribute to raising the bar on AI quality engineering practices.

What you’ll bring --------------

• 5+ years of experience in software engineering or SDET roles with an emphasis on software development.
• Strong programming skills in Python (preferred), Java, or JavaScript.
• Experience testing distributed, cloud-native SaaS systems and APIs.
• Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
• Hands-on exposure to LLMs or AI/ML systems (e.g., OpenAI, Claude, Gemini, or similar platforms).
• Understanding of non-deterministic systems and probabilistic testing approaches.
• Experience building test frameworks and scalable automation systems.
• Familiarity with AI evaluation techniques (benchmarking, golden datasets, human-in-the-loop validation).
• Experience with CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Strong collaboration skills with the ability to work across distributed teams and time zones.
• Bachelor’s degree in Computer Science or equivalent practical experience.

Backend: Python, Go, Google Cloud Platform, Cloud Run / App Engine, Kubernetes, Datastore, Redis, ElasticSearch.

• Frontend: Vue3, React.
• AI Stack: LLM APIs, LiveKit, prompt orchestration frameworks, evaluation tooling.

For exceptional talent based in British Columbia, Canada the target base salary range for this position is $150,500-$175,250 CAD.

Why Join Dialpad ---------------

• Work at the center of the AI transformation in business communications.
• Build and ship agentic AI products that are redefining how companies operate.
• Join a team where AI amplifies every employee’s impact.
• Competitive salary, comprehensive benefits, and real opportunities for growth.

We believe in investing in our people. Dialpad offers competitive benefits and perks, cutting-edge AI tools, and a robust training program that help you reach your full potential.

We have designed our offices to be inclusive, offering a vibrant environment to cultivate collaboration and connection.

Our exceptional culture, repeatedly recognized as a Great Place to Work, ensures that every employee feels valued and empowered to contribute to our collective success.

Don’t meet every single requirement? If you’re excited about this role and possess the fundamental traits, drive, and strong ambition we seek, but your experience doesn’t meet every qualification, we encourage you to apply.

Dialpad is an equal-opportunity employer. We are dedicated to creating a community of inclusion and an environment free from discrimination or harassment.

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Senior Software Engineer, AI Products (India)

Company Overview

Airbnb is a global online marketplace for booking accommodations, with over 5 million hosts and 2 billion guest arrivals.

The Community You Will Join

The Airfam Products team exists to make every Airbnb employee more productive through a unified digital headquarters experience. As part of a 13-person cross-functional team of engineers, designers, researchers, and product managers, you'll work on platforms that serve Airbnb's entire global workforce. Our portfolio includes One Airbnb (the company's internal cultural hub with enterprise search, people profiles, and AI-powered chat), OneChat (Airbnb's enterprise AI assistant enabling secure LLM interactions), and a suite of tools that power how employees discover information, connect with colleagues, and get work done. You'll be joining the AI for Non-Developers workstream, focused on expanding AI productivity tools to all Airbnb employees,building OneChat Agents, deep research capabilities, artifact creation tools, and task automation that make AI accessible to everyone, regardless of technical background.

The Difference You Will Make

As a Senior Software Engineer on the Airfam Products team, you'll be instrumental in building Airbnb's next generation of AI-powered employee experience platforms. Your work will be a force multiplier for the entire company,every AI feature you ship, every system you architect, and every engineer you mentor will amplify productivity across Airbnb's global workforce. You will:

• Democratize AI by building tools that empower non-technical employees to leverage the power of LLMs
• Drive innovation by taking AI prototypes from concept to production at scale
• Shape the future of how Airbnb employees work, collaborate, and discover information

A Typical Day

• Lead the technical design and implementation of LLM-powered features for OneChat and enterprise AI tools, including RAG pipelines, AI agents, and prompt optimization
• Partner with product managers, designers, and cross-functional teams to translate user problems into AI-powered solutions that serve Airbnb's global workforce
• Develop and iterate on agentic AI capabilities, including multi-step reasoning, tool use, and context-aware decision-making
• Implement evaluation pipelines and quality systems to measure model performance, detect hallucinations, and ensure responsible AI practices
• Own production AI systems end-to-end, including deployment strategies, monitoring, alerting, and incident response
• Collaborate with the DevAI team on AirChat SDK integrations, MCP (Model Context Protocol) implementations, and Glean Action Packs
• Mentor engineers (L6-L8) through design reviews, architecture discussions, and pair programming sessions
• Stay current with the rapidly evolving GenAI landscape, evaluating new models and techniques for potential application
• Balance hands-on technical contributions with technical leadership activities

Your Expertise

• 8+ years of software engineering experience, with significant focus on building production AI/ML systems
• 2+ years of hands-on experience with Large Language Models (LLMs), including fine-tuning, prompt engineering, embeddings, and retrieval-augmented generation (RAG)
• Strong proficiency in backend technologies (TypeScript, Go, or Java)
• Strong backend and distributed systems expertise, including API design (REST, GraphQL) and cloud infrastructure (AWS, GCP, or Azure)
• Track record of shipping AI-powered products from prototype to production
• Proven ability to collaborate cross-functionally and influence without authority
• Excellent communication skills with ability to distill complex technical concepts for diverse audiences
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

Preferred

• Master's or PhD in Computer Science, Machine Learning, or related field
• Experience building AI agents and multi-agent systems, preferably using Claude
• Experience building integrations using MCP
• Experience with containerization and orchestration (Docker, Kubernetes)
• Background in building enterprise-grade internal tools and developer productivity platforms
• Experience with frontend technologies (React, Next.js) for full-stack AI product development
• Contributions to open-source Gen AI/ML projects or publications at top venues

Your Location

This position is based in Bangalore, India with a hybrid work arrangement. You'll collaborate with teammates across global time zones, with primary alignment to Pacific Time for key meetings.

Our Commitment to Inclusion & Belonging

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

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This is a high-leverage role. The tools you build will be used by dozens of researchers and investigators, and directly shape our ability to measure and mitigate both misuse and misalignment.

You'll work across the full stack, from core analysis frameworks through user-facing apps and interfaces.

Responsibilities:

• Design and implement AI-based monitoring systems for AI training and deployment
• Extend and improve core frameworks for processing large volumes of unstructured text
• Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions
• Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings
• Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest

You May Be a Good Fit If You:

• Have 5+ years of software engineering experience, with meaningful exposure to ML systems
• Are excited about the problem of scaling human oversight of AI systems
• Are familiar with LLM application development and evaluation
• Enjoy building tools that other people use , you care about UX, reliability, and documentation
• Thrive in collaborative, cross-functional environments

Strong Candidates May Also Have:

• Experience with productionizing internal tools or building developer-facing platforms
• Background in building monitoring or observability systems
• Comfort with ambiguity , our team is small and growing, and you'll help define what we become

The annual compensation range for this role is $320,000-$405,000 USD.

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From statistical models all the way down to deep and foundational models, feature augmentation and auto-tuning, our Applied ML/AI team works on some of the most complex, most interesting problems facing businesses, making Databricks' infrastructure and products as performant and cost-efficient as possible.

This is a high-impact problem as our customers look at us to deliver the most out of their data.

Key responsibilities include:

• Building features and running end-to-end systems in a small team of experienced engineers and data scientists.
• Shaping the direction of our applied ML investment by engaging with engineering and product teams across the company.
• Driving the development and deployment of state-of-the-art ML/AI models and systems that directly impact the capabilities and performance of Databricks’ products, infrastructure, and services.
• Architecting and implementing robust, scalable ML infrastructure, including model training and serving components to support seamless integration of AI/ML models into production environments.
• Working on novel modeling techniques in the field of ML for forecasting.

What we look for:

• 2-8 years of machine learning engineering experience in high-velocity, high-growth companies.
• Strong understanding of both computer systems and statistics.
• Experience developing AI/ML systems at scale in production.
• Strong track record of ML modeling that goes beyond using standard libraries.
• Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews, and deployment.
• A large breadth of knowledge or willingness to develop mathematical modelling beyond the ML.

Why Join Us?

At Databricks, we are building state-of-the-art AI solutions that redefine how users interact with data and our products. You’ll have the opportunity to shape the future of AI-driven products at Databricks, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts.

If you're excited about pushing the boundaries of AI in real-world applications, we’d love to hear from you!

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As part of the Safeguards team, you'll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.

Responsibilities:

• Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
• Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

You may be a good fit if you:

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Strong candidates may have experience with:

• Working with large language models and modern transformer architectures
• Implementing A/B testing frameworks and experimentation infrastructure for ML systems
• Developing monitoring and alerting systems for ML model performance and data drift
• Building automated labeling systems and human-in-the-loop workflows
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements
• Contributing to open-source ML infrastructure projects

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.

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You'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

We conduct all interviews in Python, and this role may require responding to incidents on short-notice, including on weekends.

Responsibilities:

Implement and optimize post-training techniques at scale on frontier models

Conduct research to develop and optimize post-training recipes that directly improve production model quality

Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation

Develop tools to measure and improve model performance across various dimensions

Collaborate with research teams to translate emerging techniques into production-ready implementations

Debug complex issues in training pipelines and model behavior

Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

Thrive in controlled chaos and are energized, rather than overwhelmed, when juggling multiple urgent priorities

Adapt quickly to changing priorities

Maintain clarity when debugging complex, time-sensitive issues

Have strong software engineering skills with experience building complex ML systems

Are comfortable working with large-scale distributed systems and high-performance computing

Have experience with training, fine-tuning, or evaluating large language models

Can balance research exploration with engineering rigor and operational reliability

Are adept at analyzing and debugging model training processes

Enjoy collaborating across research and engineering disciplines

Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

Have experience with LLMs

Have a keen interest in AI safety and responsible deployment

We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems.

However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.

The annual compensation range for this role is $350,000-$500,000 USD.

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As a senior individual contributor, you'll be accountable for the technical decisions that ripple across the team and beyond. You'll architect the systems, tooling, and evaluation infrastructure that determine how quickly our research can move.

Responsibilities:

• Set technical direction for evaluation systems, research infrastructure, and internal tooling across the Claude Code team

• Architect eval frameworks that measure model capabilities across diverse coding tasks and scale with our research roadmap

• Lead the design of infrastructure that enables researchers to run experiments at scale, and make the foundational tradeoffs that shape how the team operates for years

• Identify the highest-leverage engineering investments,often before anyone has asked for them,and drive them to completion

• Serve as a senior technical bridge between product and research, using strong product intuition to influence which capabilities we prioritize and how we measure progress against them

• Mentor and raise the bar for other engineers on the team; review designs, unblock peers, and model the engineering standards we want to scale

• Partner with research leads to translate ambiguous research questions into durable engineering solutions

• Own critical systems end-to-end, from architecture through production reliability, and take responsibility for their long-term health

If you have 10+ years of software engineering experience, with a track record of operating as a Staff or Principal engineer (or equivalent) at a high-caliber organization, you may be a good fit for this role.

Strong candidates may also have experience with designing or scaling eval/evaluation frameworks for ML systems, reinforcement learning infrastructure or training systems, leading technical initiatives in high-performance, demanding environments, research computing, scientific infrastructure, or developer platforms at scale, a strong quantitative foundation (math, physics, or related fields), and expertise in Python and TypeScript.

The annual compensation range for this role is $405,000-$485,000 USD.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.

About the role

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.

Responsibilities

• Implement and optimize post-training techniques at scale on frontier models
• Conduct research to develop and optimize post-training recipes that directly improve production model quality
• Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
• Develop tools to measure and improve model performance across various dimensions
• Collaborate with research teams to translate emerging techniques into production-ready implementations
• Debug complex issues in training pipelines and model behavior
• Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

• Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities
• Adapt quickly to changing priorities
• Maintain clarity when debugging complex, time-sensitive issues
• Have strong software engineering skills with experience building complex ML systems
• Are comfortable working with large-scale distributed systems and high-performance computing
• Have experience with training, fine-tuning, or evaluating large language models
• Can balance research exploration with engineering rigor and operational reliability
• Are adept at analyzing and debugging model training processes
• Enjoy collaborating across research and engineering disciplines
• Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

• Have experience with LLMs
• Have a keen interest in AI safety and responsible deployment

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links,visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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Responsibilities:

• Conduct research into the science of converting compute into intelligence
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Optimize training infrastructure to improve efficiency and reliability
• Develop dev tooling to enhance team productivity

You may be a good fit if you:

• Have significant software engineering experience and a proven track record of building complex systems
• Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
• Are proficient in Python and experienced with deep learning frameworks
• Are results-oriented with a bias towards flexibility and impact
• Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team
• View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximize impact
• Care about the societal impacts of your work and have ambitious goals for AI safety and general progress

Strong candidates may have:

• Experience with JAX
• Experience with reinforcement learning
• Experience working on high-performance, large-scale ML systems
• Familiarity with accelerators, Kubernetes, and OS internals
• Experience with language modeling using transformer architectures
• Background in large-scale ETL processes
• Experience with distributed training at scale (thousands of accelerators)

Strong candidates need not have:

• Experience in all of the above areas , we value breadth of interest and willingness to learn over checking every box
• Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well
• An advanced degree , exceptional engineers with strong research instincts are equally encouraged to apply

The annual compensation range for this role is £260,000-£630,000 GBP.

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As a research engineer, you'll partner with the product org to ensure that the vision improvements you deliver impact Claude's performance on real-world tasks. You'll also work with our product org to find solutions to our most vexing API customer challenges related to vision and spatial reasoning.

Strong candidates may also have experience with large-scale pretraining, SL, and RL on language models, deep learning research on images, video, or other modalities, developing complex agentic systems using LLMs, high-performance ML systems (GPUs, TPUs, JAX, PyTorch), and large-scale ETL and data pipeline development.

The annual compensation range for this role is $350,000-$850,000 USD.

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Key responsibilities include:

• Design and execute capability evaluations to assess the capabilities of new models
• Collaborate closely with internal and external threat modeling experts to develop training data for our safety systems, and with ML engineers to train these safety systems, optimizing for both robustness against adversarial attacks and low false-positive rates for legitimate researchers
• Analyze safety system performance in traffic, identifying gaps and proposing improvements
• Develop rigorous stress-testing of our safeguards against evolving threats and product surfaces
• Partner with Research, Product, and Policy teams to ensure biological safety is embedded throughout the model development lifecycle
• Contribute to external communications, including model cards, blog posts, and policy documents related to biological safety
• Monitor emerging technologies for their potential to contribute to new risks and new mitigation strategies, and strategically address these

You may be a good fit for this role if you have a PhD in molecular biology, virology, microbiology, biochemistry, systems or computational biology, or a related life sciences field, or equivalent professional experience. You should also have extensive experience in scientific computing and data analysis, with proficiency in programming (Python preferred), deep expertise in modern biology, including both 'reading' and 'writing' techniques in biology, familiarity with dual-use research concerns, select agent regulations, and biosecurity frameworks, strong analytical and writing skills, and a passion for learning new skills and adapting to changing techniques and technologies.

Preferred qualifications include background in AI/ML systems, particularly experience with large language models, experience in developing ML for biological systems, and extensive experience in complex projects with multiple stakeholders.

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We run multiple cohorts of Fellows each year and review applications on a rolling basis. This application is for cohorts starting in July 2026 and beyond.

As a Fellow, you will receive:

• Direct mentorship from Anthropic researchers
• Access to a shared workspace in either Berkeley, California or London, UK
• Connection to the broader AI safety and security research community
• A weekly stipend of $3,850 USD / £2,310 GBP / $4,300 CAD, plus benefits
• Funding for compute and other research expenses

The interview process will include an initial application and reference check, technical assessments and interviews, and a research discussion.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

The expected base stipend for this role is $3,850 USD / £2,310 GBP / $4,300 CAD per week, with an expectation of 40 hours per week for 4 months (with possible extension).

Fellows will undergo a project selection and mentor matching process. Potential mentors include Alwin Peng and Zygi Straznickas. For a past example of an engineering-heavy project, see 'AI agents find $4.6M in blockchain smart contract exploits'.

Projects in this workstream may include building a CPU simulator for accelerator workloads, adding backends for different accelerators on an open source project, building on demand infrastructure for other infrastructure heavy fellows projects, and building complex synthetic data or environment pipelines.

To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program. Workspace locations are in London and Berkeley, and we are open to remote fellows in the UK, US, or Canada.

We do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit for full-time roles at Anthropic. In previous cohorts, 25-50% of fellows received a full-time offer, and we’ve supported many more to go on to do great work on AI safety and security at other organisations.

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You will join the multimodal team to push toward superhuman multimodal intelligence. Advance understanding and generation across modalities,image, video, audio, and text,spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.

Collaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. Contribute to building models that can see, hear, reason about, and interact with the world in real time at unprecedented levels.

Responsibilities

• Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale.
• Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text).
• Advance multimodal capabilities including spatial-temporal compression, cross-modal alignment, world modeling, reasoning, emergent abilities, audio/image/video understanding & generation, real-time video processing, and noisy data handling.
• Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models.
• Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy.
• Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance.
• Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback.
• Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions.

Basic Qualifications

• Hands-on experience with multimodal pre-training, post-training, or fine-tuning (vision, audio, video, or cross-modal).
• Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA.
• Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design).
• Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data.
• Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors).
• Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers.
• Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences.

Preferred Skills and Experience

• Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling.
• Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems.
• Proficiency in Rust and/or C++ for performance-critical components.
• Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes.
• Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership.
• Passion for end-to-end user experience in interactive, real-time multimodal AI systems.

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Your responsibilities will include owning the design and evolution of the transactional systems that power the platform, defining architecture for database and storage systems under high-throughput, low-latency workloads, making and driving decisions around data modeling, indexing, replication, and consistency, debugging and resolving complex production issues, establishing standards for reliability, observability, and operability across the platform, partnering with product and research teams to support evolving and often ambiguous requirements, driving improvements in performance, scalability, and cost across the system, mentoring engineers and raising the bar for system design and technical decision-making.

Key qualifications include 10+ years of experience building and operating production systems at scale, with ownership of critical infrastructure, strong experience designing and operating transactional systems and databases, deep understanding of data modeling, indexing, transactions, concurrency, and consistency tradeoffs, experience owning systems with strict reliability and performance requirements in production, strong experience debugging complex production issues and reasoning about failure modes, experience designing distributed systems or large-scale infrastructure where tradeoffs are non-trivial, proven ability to define architecture and drive technical decisions end-to-end, strong judgment in balancing performance, reliability, and cost, ability to operate effectively in ambiguous, fast-moving environments with high ownership.

Preferred qualifications include experience with database internals, storage systems, or query engines, experience building infrastructure for AI/ML systems or data platforms, experience in early-stage or high-growth environments.

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You'll work at the intersection of all major components of the XBat systems, providing user-facing applications to operate XBat and will be collaborating closely with our Aircraft Software, Hivemind, and Flight Test teams to design this state of the art ground system.

Responsibilities:

• Design, develop, and maintain software systems for the XBat Platform
• Build robust ground control stations, mission planning tools, and communication interfaces
• Collaborate with Aircraft Software, Hivemind, and Flight Test teams to design the ground system
• Provide user-facing applications to operate XBat

Requirements:

• Bachelor's degree in Computer Science, Software Engineering, Robotics, or a related field with 5+ years of professional software development experience, or Masters with 4+ years, or PhD with 2+ years
• Strong proficiency in one or more languages: C++, Python, or JavaScript/TypeScript
• Experience building distributed systems or networked applications
• Previous experience designing and developing ground control station (GCS) software for critical mission applications
• Experience developing user interfaces (e.g., Qt, React, or similar frameworks)
• Strong debugging and system integration skills
• Ability to work in a fast-paced, cross-functional engineering environment
• Ability to obtain security clearance

Preferred Qualifications:

• Experience with UAS platforms
• Experience with AI/ML systems
• Exposure to cybersecurity best practices for embedded or distributed systems

$110,000 - $200,000 a year

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Our team is focused on making boats go and perform tasks with no human involvement. This job is available at multiple levels, including entry, senior, and staff.

The successful candidate will develop algorithms and models which allow boats to sense and navigate, as well as develop metrics which allow quantitative analysis of improvements and regressions in boat performance.

Responsibilities:

• Develop algorithms and models which allow boats to sense and navigate
• Develop metrics which allow quantitative analysis of improvements and regressions in boat performance
• Analyze and work with large data systems to enable model training and evaluation

Requirements:

• Strong programming fundamentals
• Extensive programming experience and demonstrated ability to work on large systems
• Computing Fundamentals
• A general understanding of operating systems and or similar large scale systems
• An understanding of basic computer architecture
• A demonstrated willingness to learn and pivot based on new information

Useful Skills:

• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)
• Understanding of various filters and their applications
• Proficiency in Rust
• Experience with maritime or autonomous vehicle projects
• Experience with signals processing or sensor fusion
• Experience with low latency inference and tracking pipelines
• Experience with path planning algorithms
• Experience training and deploying multi modal models
• Experience with various sensors including radar, cameras, and lidar
• Experience developing and optimizing deployed ML systems

Physical Demands:

• Prolonged periods of sitting at a desk and working on a computer
• Occasional standing and walking within the office
• Manual dexterity to operate a computer keyboard, mouse, and other office equipment
• Visual acuity to read screens, documents, and reports
• Occasional reaching, bending, or stooping to access file drawers, cabinets, or office supplies
• Lifting and carrying items up to 20 pounds occasionally (e.g., office supplies, packages)

Benefits:

• Medical Insurance: Comprehensive health insurance plans covering a range of services
• Dental and Vision Insurance: Coverage for routine dental check-ups, orthodontics, and vision care
• Saronic pays 100% of the premium for employees and 80% for dependents
• Time Off: Generous PTO and Holidays
• Parental Leave: Paid maternity and paternity leave to support new parents
• Competitive Salary: Industry-standard salaries with opportunities for performance-based bonuses
• Retirement Plan: 401(k) plan
• Stock Options: Equity options to give employees a stake in the company’s success
• Life and Disability Insurance: Basic life insurance and short- and long-term disability coverage
• Additional Perks: Free lunch benefit and unlimited free drinks and snacks in the office

Additional Information:

This role requires access to export-controlled information or items that require “U.S. Person” status. As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3).

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Responsibilities:

• Architect, implement, and maintain foundational scientific computing libraries that will be used in KoBold’s mineral exploration analyses.
• Build tooling to increase the velocity of our machine learning progress, including enabling rapid prototyping in Jupyter notebooks; build experimentation, evaluation, and simulation frameworks; turning successful R&D into robust, scalable ML pipelines; and organizing models and their outputs for repeatability and discoverability.
• In collaboration with data scientists, build models to make statistically valid predictions about the locations of economic concentrations of ore metals within the Earth’s crust.
• Apply–and coach team members to use–engineering best practices such as writing robust, testable and composable code
• Collaborate with data scientists, geoscientists and engineers to invent the modern scientific computing stack for mineral exploration
• Occasional travel to exploration sites around the world to observe the impact of scientific computing on KoBold’s exploration products and design new technologies to further discovery. Travel is approximately twice per year depending on project needs.

Qualifications:

• At least 5 years of experience as a software engineer, data scientist or ML engineer, though most great candidates will have closer to 10.
• Track record of building production quality data processing solutions or tooling that have delivered business value
• Proficiency with foundational concepts of ML, including statistical, traditional and deep-learning approaches
• Proficiency in Python, ideally including array-based packages such as xarray and numpy
• Deep experience with measured scientific data
• Experience in visualizing scientific data for domain experts
• Experience in MLops and in the making of robust ML systems
• Drive to increase the velocity and effectiveness of our data scientists in both experimental and production workflows
• Capacity to dive deep on novel challenging problems in applying ML to mineral exploration, including understanding a complex domain of geology and mineral exploration practices as well as working with limited, disparate and noisy data sources
• Collaborative attitude to work with stakeholders with different backgrounds (data scientists, geoscientists, software engineers, operations)

Work practices and motivation:

• Ability to take ownership and responsibility of large projects.
• Intellectual curiosity and eagerness to learn about all aspects of mineral exploration, particularly in the geology domain. Open to working directly with geologists in the field. Enjoys constantly learning such that you are driving insights and innovations.
• Ability to explain technical problems to and collaborate on solutions with domain experts who aren’t software developers. A strong communicator who enjoys working with colleagues across the company.
• Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on a range of responsibilities.
• Keen not just to build cool technology, but to figure out what technical product to build to best achieve the business objectives of the company.
• Ability to independently prioritize multiple tasks effectively.

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This role will support compliance management in reviewing new products and services to ensure they are compliant with BSA/AML and consumer protection regulations and understanding the types of controls that would be needed in order to mitigate AML risk. Most importantly, they will serve as our growing department’s ‘lead,’ offering support and expertise in financial crimes compliance.

Your day-to-day:

• Own and optimize AML transaction monitoring and screening systems (e.g., rules tuning, threshold adjustments, alert calibration).
• Analyze alert and case data to identify trends, gaps, and opportunities to improve detection efficiency and effectiveness.
• Investigate and clear transaction monitoring alerts.
• Partner with Product, Data, and Engineering teams to design, implement, and enhance AML controls.
• Act as the primary point of contact for AML vendors, managing system performance, enhancements, and issue management.
• Conduct ongoing validation and testing of monitoring scenarios to ensure alerts are accurate, risk-aligned, and defensible.
• Support model/rule governance, including documentation, tuning rationale, and audit/regulatory readiness.
• Perform quality assurance on alerts, cases, and SAR/UAR outputs to ensure consistency and regulatory compliance.
• Contribute to the development and enhancement of AML policies, procedures, and risk assessments.
• Assist with audits, regulatory exams, and internal reviews related to transaction monitoring and screening systems.
• Identify and drive automation and process improvements across AML operations and systems.
• Support ad-hoc analyses and strategic initiatives as Greenlight scales.

What you’ll bring to the team:

The position will be fast-paced with no day being the same as the one before, so we are looking for a fun, energetic individual who is passionate about financial crimes and compliance in addition to the below:

• 5–7 years of AML/BSA/CTF experience within a bank, fintech, or money services business.
• Strong hands-on experience with AML systems (transaction monitoring and/or screening), including rules tuning, scenario development, or alert optimization.
• Demonstrated experience working with AML data (alert volumes, false positives, typologies, segmentation, etc.).
• Experience working with or managing third-party AML vendors (e.g., ComplyAdvantage, Oscilar, Actimize, Verafin, etc.).
• Familiarity with model/rule validation, tuning methodologies, and/or system implementation is strongly preferred.
• CAMS, CFE, or similar certification preferred.
• Solid understanding of BSA/AML regulations, OFAC, and suspicious activity reporting requirements.
• Ability to translate regulatory requirements and risk insights into practical system rules and controls.
• Strong analytical mindset with experience using data to drive decisions (SQL or data tooling is a plus, but not required).
• Comfortable working cross-functionally with technical and non-technical stakeholders.
• Ability to operate independently in a fast-paced, evolving environment.

Work perks at Greenlight:

• Medical, dental, vision, and HSA match
• Paid life insurance, AD&D, and disability benefits
• Traditional 401k with company match
• Unlimited PTO
• Paid company holidays and pop-up bonus holidays
• Professional development stipends
• Mental health resources
• 1:1 financial planners
• Fertility healthcare
• 100% paid parental and caregiving leave, plus cleaning service and meals during your leave
• Flexible WFH, both remote and in-office opportunities
• Fully stocked kitchen, catered lunches, and occasional in-office happy hours
• Employee resource groups

Additional Information

Our stance on salaries: Greenlight provides a competitive compensation package with a market-based approach to pay and will vary depending on your location, experience and skill set. The total compensation package for this position will also include a discretionary performance bonus, equity rewards, medical benefits, 401K match, and more.

Greenlight conducts continuous compensation evaluations across departments and geographies to ensure we are keeping our pay current and competitive.

The estimated base pay range for this position in (NY, CA, WA): $60,000 - $80,000

The estimated base pay range for this position in (CO): $60,000 - $70,000

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About the Team

We build foundational AI infrastructure that enables large-scale training and inference across diverse workloads and rapidly evolving hardware generations. Our work directly shapes how AI systems are designed, deployed, and scaled today and into the future. Engineers on this team operate with end-to-end ownership, deep technical rigor, and a strong bias toward real-world impact.

Microsoft Superintelligence Team

Microsoft Superintelligence team’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

This role is part of Microsoft AI’s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models!

Responsibilities

Lead the co-design of AI systems across hardware and software boundaries, spanning accelerators, interconnects, memory systems, storage, runtimes, and distributed training/inference frameworks. Drive architectural decisions by analyzing real workloads, identifying bottlenecks across compute, communication, and data movement, and translating findings into actionable system and hardware requirements. Co-design and optimize parallelism strategies, execution models, and distributed algorithms to improve scalability, utilization, reliability, and cost efficiency of large-scale AI systems. Develop and evaluate what-if performance models to project system behavior under future workloads, model architectures, and hardware generations, providing early guidance to hardware and platform roadmaps. Partner with compiler, kernel, and runtime teams to unlock the full performance of current and next-generation accelerators, including custom kernels, scheduling strategies, and memory optimizations. Influence and guide AI hardware design at system and silicon levels, including accelerator microarchitecture, interconnect topology, memory hierarchy, and system integration trade-offs. Lead cross-functional efforts to prototype, validate, and productionize high-impact co-design ideas, working across infrastructure, hardware, and product teams. Mentor senior engineers and researchers, set technical direction, and raise the overall bar for systems rigor, performance engineering, and co-design thinking across the organization.

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We pursue this mission through deep hardware–software co-design, combining rigorous systems thinking with hands-on engineering. The team invests heavily in understanding real production workloads large-scale training, inference, and emerging multimodal models and translating those insights into concrete improvements across the stack: from kernels, runtimes, and distributed systems, all the way down to silicon-level trade-offs and datacenter-scale architectures.

This role sits at the boundary between exploration and production. You will work closely with internal infrastructure, hardware, compiler, and product teams, as well as external partners across the hardware and systems ecosystem. Our operating model emphasizes rapid ideation and prototyping, followed by disciplined execution to drive high-leverage ideas into production systems that operate at massive scale.

In addition to delivering real-world impact on large-scale AI platforms, the team actively contributes to the broader research and engineering community. Our work aligns closely with leading communities in ML systems, distributed systems, computer architecture, and high-performance computing, and we regularly publish, prototype, and open-source impactful technologies where appropriate.

Microsoft Superintelligence Team Microsoft Superintelligence team’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

This role is part of Microsoft AI’s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact.

Responsibilities Lead the co-design of AI systems across hardware and software boundaries, spanning accelerators, interconnects, memory systems, storage, runtimes, and distributed training/inference frameworks.

Drive architectural decisions by analyzing real workloads, identifying bottlenecks across compute, communication, and data movement, and translating findings into actionable system and hardware requirements.

Co-design and optimize parallelism strategies, execution models, and distributed algorithms to improve scalability, utilization, reliability, and cost efficiency of large-scale AI systems.

Develop and evaluate what-if performance models to project system behavior under future workloads, model architectures, and hardware generations, providing early guidance to hardware and platform roadmaps.

Partner with compiler, kernel, and runtime teams to unlock the full performance of current and next-generation accelerators, including custom kernels, scheduling strategies, and memory optimizations.

Influence and guide AI hardware design at system and silicon levels, including accelerator microarchitecture, interconnect topology, memory hierarchy, and system integration trade-offs.

Lead cross-functional efforts to prototype, validate, and productionize high-impact co-design ideas, working across infrastructure, hardware, and product teams.

Mentor senior engineers and researchers, set technical direction, and raise the overall bar for systems rigor, performance engineering, and co-design thinking across the organization.

Qualifications Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Additional or Preferred Qualifications Master’s Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor’s Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Strong background in one or more of the following areas: AI accelerator or GPU architectures Distributed systems and large-scale AI training/inference High-performance computing (HPC) and collective communications ML systems, runtimes, or compilers Performance modeling, benchmarking, and systems analysis Hardware–software co-design for AI workloads Proficiency in systems-level programming (e.g., C/C++, CUDA, Python) and performance-critical software development.

Proven ability to work across organizational boundaries and influence technical decisions involving multiple stakeholders. Experience designing or operating large-scale AI clusters for training or inference. Deep familiarity with LLMs, multimodal models, or recommendation systems, and their systems-level implications. Experience with accelerator interconnects and communication stacks (e.g., NCCL, MPI, RDMA, high-speed Ethernet or InfiniBand). Background in performance modeling and capacity planning for future hardware generations. Prior experience contributing to or leading hardware roadmaps, silicon bring-up, or platform architecture reviews. Publications, patents, or open-source contributions in systems, architecture, or ML systems are a plus.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

We are looking for biological scientists to help build safety and oversight mechanisms for our AI systems. As a Safeguards Biological Safety Research Scientist, you will apply your technical skills to design and develop our safety systems which detect harmful behaviours and to prevent misuse by sophisticated threat actors. You will be at the forefront of defining what responsible AI safety looks like in the biological domain, working across research, policy, and engineering to translate complex biosecurity concepts into concrete technical safeguards. This is a unique opportunity to shape how frontier AI models handle dual-use biological knowledge—balancing the tremendous potential of AI to accelerate legitimate life sciences research while preventing misuse by sophisticated threat actors.

In this role, you will:

• Design and execute capability evaluations ('evals') to assess the capabilities of new models
• Collaborate closely with internal and external threat modeling experts to develop training data for our safety systems, and with ML engineers to train these safety systems, optimising for both robustness against adversarial attacks and low false-positive rates for legitimate researchers
• Analyse safety system performance in traffic, identifying gaps and proposing improvements
• Develop rigorous stress-testing of our safeguards against evolving threats and product surfaces
• Partner with Research, Product, and Policy teams to ensure biological safety is embedded throughout the model development lifecycle
• Contribute to external communications, including model cards, blog posts, and policy documents related to biological safety
• Monitor emerging technologies for their potential to contribute to new risks and new mitigation strategies, and strategically address these

You may be a good fit if you have

• A PhD in molecular biology, virology, microbiology, biochemistry, systems or computational biology, or a related life sciences field, OR equivalent professional experience
• Extensive experience in scientific computing and data analysis, with proficiency in programming (Python preferred)
• Deep expertise in modern biology, including both 'reading' (e.g. high-throughput measurement, functional assays) and 'writing' (gene synthesis, genome editing, strain construction, protein engineering) techniques in biology
• Familiarity with dual-use research concerns, select agent regulations, and biosecurity frameworks (e.g., Biological Weapons Convention, Australia Group guidelines)
• Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders
• Have a passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies
• Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve

Preferred Qualifications

• Background in AI/ML systems, particularly experience with large language models
• Experience in developing ML for biological systems
• Extensive experience in complex projects with multiple stakeholders

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role:

Anthropic's performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training. As an Engineering Manager on these teams you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems. You also will help bring clarity, focus, and context to your teams in a fast paced, dynamic environment.

Responsibilities:

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
• Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
• Manage day-to-day execution of the team's work
• Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
• Coach and support your reports in understanding, and pursuing, their professional growth
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Have a background in machine learning, AI, or a similar related technical field
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
• Have experience managing teams through periods of rapid growth and change
• Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you’ll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship:

We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification.

Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and paid time off, and a comprehensive benefits package.

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As a TPU Kernel Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimising kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance.

You may be a good fit if you:

• Have significant experience optimising ML systems for TPUs, GPUs, or other accelerators
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• Designing and implementing kernels for TPUs or other ML accelerators
• Understanding accelerators at a deep level, e.g. a background in computer architecture
• ML framework internals
• Language modeling with transformers

Representative projects:

• Implement low-latency, high-throughput sampling for large language models
• Adapt existing models for low-precision inference
• Build quantitative models of system performance
• Design and implement custom collective communication algorithms
• Debug kernel performance at the assembly level

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

Anthropic is seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You'll contribute across the entire stack, from low-level optimizations to high-level algorithm and experimental design, balancing research goals with practical engineering constraints.

Responsibilities:

• Conduct research into the science of converting compute into intelligence
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyse scientific experiments to advance our understanding of large language models
• Optimise training infrastructure to improve efficiency and reliability
• Develop dev tooling to enhance team productivity

You may be a good fit if you:

• Have significant software engineering experience and a proven track record of building complex systems
• Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
• Are proficient in Python and experienced with deep learning frameworks
• Are results-oriented with a bias towards flexibility and impact
• Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team
• View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximise impact
• Care about the societal impacts of your work and have ambitious goals for AI safety and general progress

Strong candidates may have:

• Experience with JAX
• Experience with reinforcement learning
• Experience working on high-performance, large-scale ML systems
• Familiarity with accelerators, Kubernetes, and OS internals
• Experience with language modeling using transformer architectures
• Background in large-scale ETL processes
• Experience with distributed training at scale (thousands of accelerators)

Strong candidates need not have:

• Experience in all of the above areas — we value breadth of interest and willingness to learn over checking every box
• Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well
• An advanced degree — exceptional engineers with strong research instincts are equally encouraged to apply

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including

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We are looking for a cybersecurity expert to lead our efforts to prevent AI misuse in the cyber domain. As a Cyber Harms Technical Policy Manager, you will lead a team applying deep technical expertise to inform the design of safety systems that detect harmful cyber behaviours and prevent misuse by sophisticated threat actors.

In this role, you will:

• Lead and grow a team of technical specialists focused on cyber threat modelling and evaluation frameworks
• Design and oversee execution of capability evaluations ('evals') to assess the cyber-relevant capabilities of new models
• Create comprehensive cyber threat models, including attack vectors, exploit chains, precursor identification, and weaponization techniques
• Develop and iterate on usage policies that govern responsible use of our models for emerging capabilities and use cases related to cyber harms
• Serve as the primary domain expert on cyber harms, advising cross-functional teams on threat landscapes and mitigation strategies
• Collaborate closely with internal and external threat modelling experts to develop training data for safety systems, and with ML engineers to train these systems, optimising for both robustness against adversarial attacks and low false-positive rates for legitimate security researchers
• Analyse safety system performance in traffic, identifying gaps and proposing improvements
• Conduct regular reviews of existing policies and enforcement systems to identify and address gaps and ambiguities related to cybersecurity risks
• Develop rigorous stress-testing of safeguards against evolving cyber threats and product surfaces
• Partner with Research, Product, Policy, Security Team, and Frontier Red Team to ensure cybersecurity safety is embedded throughout the model development lifecycle
• Translate cybersecurity domain knowledge into actionable safety requirements and clearly articulated policies
• Contribute to external communications, including model cards, blog posts, and policy documents related to cybersecurity safety
• Monitor emerging technologies and threat landscapes for their potential to contribute to new risks and mitigation strategies, and strategically address these
• Mentor and develop team members, fostering a culture of technical excellence and responsible AI development

You may be a good fit if you have:

• An M.S. or PhD in Computer Science, Cybersecurity, or a related technical field, OR equivalent professional experience in offensive or defensive cybersecurity
• 5+ years of hands-on experience in cybersecurity, with deep expertise in areas such as vulnerability research, exploit development, network security, malware analysis, or penetration testing
• 2+ years of experience managing technical teams or leading complex technical projects with multiple stakeholders
• Experience in scientific computing and data analysis, with proficiency in programming (Python preferred)
• Deep expertise in modern cybersecurity, including both offensive techniques (vulnerability research, exploit development, penetration testing, malware analysis) and defensive measures (detection, monitoring, incident response)
• Demonstrated ability to create threat models and translate technical cyber risks into policy frameworks
• Familiarity with responsible disclosure practices, vulnerability coordination, and cybersecurity frameworks (e.g., MITRE ATT&CK, NIST Cybersecurity Framework, CWE/CVE systems)
• Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders
• Experience developing policies or guidelines at scale, balancing safety concerns with enabling legitimate use cases
• A passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies
• Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve
• Track record of translating specialised technical knowledge into actionable safety policies or enforcement guidelines

Preferred Qualifications:

• Background in AI/ML systems, particularly experience with large language models
• Experience developing ML-based security systems or adversarial ML research
• Experience working with defence, intelligence, or security organisations (e.g., NSA, CISA, national labs, security contractors)
• Published security research, disclosed vulnerabilities, or participated in bug bounty programs
• Understanding of Trust & Safety operations and content moderation at scale
• Certifications such as OSCP, OSCE, GXPN, or equivalent demonstrating technical depth
• Understanding of dual-use security research concerns and ethical considerations in AI safety

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Team

As AI training and deployments scale, the volume of data we need to monitor and understand is exploding. Our team uses Claude itself to make sense of this data. We own an integrated set of tools enabling Anthropic to ask open-ended questions, surface unexpected patterns, and maintain meaningful human oversight over massive datasets.

Our tools are widely adopted internally — powering ongoing enforcement, threat intelligence investigations, model audits, and more — and we’re looking for experienced engineers and researchers to both scale up existing applications and go zero-to-one on new ones.

About the Role

As a Research Engineer on our team, you'll design and build systems that let AI analyse large, unstructured datasets — think tens or hundreds of thousands of conversations or documents — and produce structured, trustworthy insights. You'll work across the full stack, from core analysis frameworks through user-facing apps and interfaces.

This is a high-leverage role. The tools you build will be used by dozens of researchers and investigators, and directly shape our ability to measure and mitigate both misuse and misalignment.

Responsibilities:

• Design and implement AI-based monitoring systems for AI training and deployment

• Extend and improve core frameworks for processing large volumes of unstructured text

• Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions

• Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings

• Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest

You May Be a Good Fit If You:

• Have 5+ years of software engineering experience, with meaningful exposure to ML systems

• Are excited about the problem of scaling human oversight of AI systems

• Are familiar with LLM application development (context engineering, evaluation, orchestration)

• Enjoy building tools that other people use — you care about UX, reliability, and documentation

• Can context-switch between deep infrastructure work and user-facing product thinking

• Thrive in collaborative, cross-functional environments

Strong Candidates May Also Have:

• Research experience in AI safety, alignment, or responsible deployment

• Practical experience with both data science and engineering, including developing and using large-scale data processing frameworks

• Experience with productionizing internal tools or building developer-facing platforms

• Background in building monitoring or observability systems

• Comfort with ambiguity — our team is small and growing, and you'll help define what we become

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role:

Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.

This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimisation, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow.

Responsibilities:

• Own critical aspects of our production pretraining pipeline, including model operations, performance optimisation, observability, and reliability
• Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
• Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
• Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
• Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
• Add new capabilities to the training codebase, such as long context support or novel architectures
• Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
• Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned

You May Be a Good Fit If You:

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
• Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
• Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
• Excel at debugging complex, ambiguous problems across multiple layers of the stack
• Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
• Are passionate about the work itself and want to refine your craft as a research engineer
• Care about the societal impacts of AI and responsible scaling

Strong Candidates May Also Have:

• Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
• Contributed to open-source LLM frameworks (e.g., open\_lm, llm-foundry, mesh-transformer-jax)
• Published research on model training, scaling laws, or ML systems
• Experience with production ML systems, observability tools, or evaluation infrastructure
• Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence

What Makes This Role Unique:

This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.

However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.

We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification.

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The Environment Scaling team is a team of researchers and engineers whose goal is to improve the intelligence of our public models for novel verticals and use cases. The team builds the training environments that fuel RL at scale. This is a unique role that combines executing directly on ML research, data operations, and project management to improve our models. You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.

Responsibilities:

• Improve and execute our fine-tuning strategies for adapting Claude to new domains and tasks
• Manage technical relationships with external data vendors, including evaluation of data quality and reward design
• Collaborate with domain experts to design data pipelines and evaluations
• Explore novel ways of creating RL environments for high value tasks
• Develop and improve QA frameworks to catch reward hacking and ensure environment quality
• Partner with other RL research teams and product teams to translate capability goals into training environments and evals

You may be a good fit if you:

• Have experience with fine-tuning large language models for specific domains or real-world use cases and/or domain expertise in an area where we would like to make our models more useful.
• Have experience with reinforcement learning, reward design, or training data curation for LLMs
• Are comfortable managing technical vendor relationships and iterating quickly on feedback
• Find value in reading through datasets to understand them and spot issues
• Have strong project management and interpersonal skills
• Are passionate about making AI more useful and accessible across different industries
• Are excited about a role that includes a combination of ML research, data operations, and project management

Strong candidates may also:

• Have experience training production ML systems
• Be familiar with distributed systems and cloud infrastructure
• Have domain expertise in an area where we would like to make our models more useful
• Have experience working with external vendors or technical partners

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco, CA.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

We are looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic's Responsible Scaling Policy commitments.

Responsibilities

• Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on
• Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts
• Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks
• Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse

You may be a good fit if you

• Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry
• Have proficiency in Python and experience building ML systems
• Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems
• Are worried about misuse risks of AI systems, and want to work to mitigate them
• Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders

Strong candidates may also have experience with

• Language modeling and transformers
• Building classifiers, anomaly detection systems, or behavioral ML
• Adversarial machine learning or red-teaming
• Interpretability or probes
• Reinforcement learning
• High-performance, large-scale ML systems

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship

We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification.

Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

The Account Abuse team is tasked with ensuring Anthropic's computing capacity is allocated fairly, minimising resources available to bad actors and preventing them from coming back. As a software engineer on this team, you will build systems that gather and analyse signals at scale, balancing tradeoffs and coordinating closely with stakeholder teams throughout the company. The ideal candidate can see things from opponents' perspectives, understand their means and motives, and anticipate their responses to countermeasures.

Responsibilities:

• Ability to think and respond quickly in a rapidly-changing greenfield environment
• Jumping into other teams' code to identify key points to gather signals or introduce interventions with minimal impact on their systems' stability, complexity, or overall architecture
• Integration with third-party data-enrichment vendors
• Creating monitoring dashboards, alerts, and internal admin UX
• Working closely with our data scientists to maintain situational awareness of our current usage patterns and trends, and with our Policy & Enforcement team to maximise the impact of their human-review availability
• Building robust and reliable multi-layered defenses
• Lead root cause analyses and deep-dive investigations into account activity to identify abuse patterns, uncover emerging attack vectors, and inform both immediate enforcement actions and longer-term systemic defenses

You may be a good fit if you have:

• A Bachelor's degree in Computer Science, Software Engineering or comparable experience
• 5-10+ years of experience in a software engineering position, preferably with a focus on integrity, spam, fraud, or abuse detection.
• Proficiency in Python, SQL, and data analysis tools.
• Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders

Strong candidates may also:

• Have experience building trust and safety mechanisms for and using AI/ML systems, such as fraud-detection models or security monitoring tools or the infrastructure to support these systems at scale
• Have worked closely with operational teams to build custom internal tooling

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation

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Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.

_Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends._

Responsibilities:

• Implement and optimize post-training techniques at scale on frontier models

• Conduct research to develop and optimize post-training recipes that directly improve production model quality

• Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation

• Develop tools to measure and improve model performance across various dimensions

• Collaborate with research teams to translate emerging techniques into production-ready implementations

• Debug complex issues in training pipelines and model behavior

• Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

• Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities

• Adapt quickly to changing priorities

• Maintain clarity when debugging complex, time-sensitive issues

• Have strong software engineering skills with experience building complex ML systems

• Are comfortable working with large-scale distributed systems and high-performance computing

• Have experience with training, fine-tuning, or evaluating large language models

• Can balance research exploration with engineering rigor and operational reliability

• Are adept at analyzing and debugging model training processes

• Enjoy collaborating across research and engineering disciplines

• Can navigate ambiguity and make progress in fast-moving research environments

Strong candidates may also:

• Have experience with LLMs

• Have a keen interest in AI safety and responsible deployment

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

You may be a good fit if you:

• Have significant software engineering experience
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPUs, Kubernetes, Pytorch, or OS internals
• Language modeling with transformers
• Reinforcement learning
• Large-scale ETL

Representative projects:

• Optimizing the throughput of a new attention mechanism
• Comparing the compute efficiency of two Transformer variants
• Making a Wikipedia dataset in a format models can easily consume
• Scaling a distributed training job to thousands of GPUs
• Writing a design doc for fault tolerance strategies
• Creating an interactive visualization of attention between tokens in a language model

Annual compensation range for this role is listed below.

Annual Salary:

$350,000 - $500,000USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in California, USA.

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Running machine learning (ML) algorithms at our scale often requires solving novel systems problems. As a Performance Engineer, you'll be responsible for identifying these problems, and then developing systems that optimize the throughput and robustness of our largest distributed systems. Strong candidates here will have a track record of solving large-scale systems problems and will be excited to grow to become an expert in ML also.

You may be a good fit if you:

• Have significant software engineering or machine learning experience, particularly at supercomputing scale
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

Representative projects:

• Implement low-latency high-throughput sampling for large language models
• Implement GPU kernels to adapt our models to low-precision inference
• Write a custom load-balancing algorithm to optimize serving efficiency
• Build quantitative models of system performance
• Design and implement a fault-tolerant distributed system running with a complex network topology
• Debug kernel-level network latency spikes in a containerized environment

Deadline to apply:

None. Applications will be reviewed on a rolling basis.

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process

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We're looking for research engineers with a strong computer vision background who believe that visual and spatial reasoning are core to fully unlocking the capabilities of LLMs. In this role, you'll work on research, development, and evaluation for state-of-the-art Claude models, with a focus on visual and spatial capabilities.

What you'll do:

• Run experiments to evaluate architectural variants, data strategies, and SL and RL techniques to improve Claude's vision

• Develop and test tools, skills, and agentic infrastructure that enable Claude to reason over visual inputs

• Create evaluations and benchmarks that measure progress on multimodal capabilities across training and deployment

• Work with our product org to find solutions to our most vexing API customer challenges related to vision and spatial reasoning

You may be a good fit if you:

• Have 7+ years of ML, computer vision, and software engineering experience through industry, academia, or other projects

• Are familiar with the architecture, training, and operation of large vision language models

• Have experience creating and evaluating large synthetic and real-world visual training datasets

• Have experience engaging in systematic prompting, finetuning, or evaluation

• Are results-oriented, with a bias towards flexibility and impact

• Enjoy pair programming and cross-team collaboration

• Care about the societal impacts of your work

Strong candidates may also have experience with:

• Large-scale pretraining, SL, and RL on language models

• Deep learning research on images, video, or other modalities

• Developing complex agentic systems using LLMs

• High-performance ML systems (GPUs, TPUs, JAX, PyTorch)

• Large-scale ETL and data pipeline development

Representative projects:

• Running experiments to determine ideal training datamixes and parameters for a synthetically generated vision dataset

• Finetuning Claude to maximise its performance using a particular set of agent tools/skills

• Building a pipeline to ingest and process a novel source of visual training data

• Designing and running experiments to evaluate the scalability of two architectural variants

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research can be found on our website.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role:

Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.

This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow.

Responsibilities:

• Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
• Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
• Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
• Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
• Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
• Add new capabilities to the training codebase, such as long context support or novel architectures
• Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
• Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned

You May Be a Good Fit If You:

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
• Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
• Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
• Excel at debugging complex, ambiguous problems across multiple layers of the stack
• Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
• Are passionate about the work itself and want to refine your craft as a research engineer
• Care about the societal impacts of AI and responsible scaling

Strong Candidates May Also Have:

• Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
• Contributed to open-source LLM frameworks (e.g., open\_lm, llm-foundry, mesh-transformer-jax)
• Published research on model training, scaling laws, or ML systems
• Experience with production ML systems, observability tools, or evaluation infrastructure
• Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence

What Makes This Role Unique:

This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.

However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.

We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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Anthropic's performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training. As an Engineering Manager on these teams, you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems. You also will help bring clarity, focus, and context to your teams in a fast-paced, dynamic environment.

Responsibilities:

• Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
• Become familiar with the team's technical stack enough to make targeted contributions as an individual contributor
• Manage day-to-day execution of the team's work
• Prioritize the team's work and manage projects in a highly dynamic, fast-paced environment
• Coach and support your reports in understanding, and pursuing, their professional growth
• Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

• Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
• Have a background in machine learning, AI, or a similar related technical field
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships with stakeholders at all levels
• Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
• Have experience managing teams through periods of rapid growth and change
• Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• GPU/Accelerator programming
• ML framework internals
• OS internals
• Language modeling with transformers

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic, we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave policies, and a dynamic and inclusive work environment.

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Location

San Francisco; New York City

Employment Type

Full time

Location Type

On-site

Department

Data Science

Compensation

• $293K – $515K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

Our B2B product organisation is building the systems that will accelerate and automate how companies operate using AI coworkers. Our vision is to enable companies to scale to $1B+ with teams of fewer than five people by leveraging AI agents and automation. We are building the infrastructure, products, and analytics that allow organisations to deploy fleets of AI agents, automate workflows, and dramatically increase employee productivity. This team sits at the centre of the future of work: defining how humans collaborate with AI systems across companies, workflows, and knowledge.

About the Role

We are looking for a product-focused Data Scientist to help shape the next generation of AI-powered enterprise products, including ChatGPT Enterprise and agentic automation systems.

This is not a traditional analytics role. You will work directly with product managers, engineers, and researchers to define how AI agents behave, how organisations adopt them, and how we measure their impact on work.

You’ll analyse how users interact with AI systems, inform product strategy, and help build the measurement frameworks that guide the future of enterprise AI.

In this role, you will:

• Partner closely with product, engineering, and research teams to shape AI-powered enterprise products

• Define metrics and measurement frameworks for AI agents, automation workflows, and enterprise adoption

• Analyse how organisations use AI tools and identify opportunities to improve product experience and business impact

• Drive insights around enterprise adoption, retention, and value realisation

• Inform product decisions around agent orchestration, enterprise workflows, and AI productivity tools

• Design experiments and evaluations that improve AI system performance and user outcomes

• Help define how companies operate with AI coworkers and automated workflows

What We’re Looking For

Must Have:

• Strong product analytics or data science experience

• Deep curiosity about AI systems and how they change how work gets done

• Ability to work closely with product and engineering teams in 0→1 product environments

• Strong analytical skills (experimentation, behavioural analysis, product metrics)

• Builder mindset — someone who wants to shape products, not just analyse them

Nice to Have:

• Experience with B2B SaaS or enterprise analytics

• Experience with developer platforms, APIs, or technical products

• Background in AI/ML systems, agent frameworks, or LLM-based products

• Systems thinking around how complex tools and workflows operate at scale

Who Thrives Here:

• AI-native — actively experimenting with and building with AI tools

• Product thinkers who want to shape the future of work

• Builders who enjoy early-stage ambiguity and 0→1 problems

• Operators who can work independently and drive large parts of a product area

Impact

You will help define:

• How organisations deploy and manage AI agents

• How AI transforms enterprise productivity

• The measurement systems behind AI-native companies

• This is an opportunity to help create the “Codex moment” for non-coding work — defining how businesses operate in an AI-first world.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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What you'll do

Develop APIs for AI inference that will be used by both internal and external customers.

• Develop APIs for AI inference that will be used by both internal and external customers
• Benchmark and address bottlenecks throughout our inference stack
• Improve the reliability and observability of our systems and respond to system outages
• Explore novel research and implement LLM inference optimizations

What you need

• Experience with ML systems and deep learning frameworks (e.g. PyTorch, TensorFlow, ONNX)
• Familiarity with common LLM architectures and inference optimization techniques (e.g. continuous batching, quantization, etc.)
• Understanding of GPU architectures or experience with GPU kernel programming using CUDA

Why this matters

As an AI Inference engineer, you will play a critical role in the development and deployment of our machine learning models. Your work will have a direct impact on the performance and reliability of our systems, and will help us to continue to innovate and improve our products.

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What you'll do

You will own the technical direction and execution of our inference systems while building and leading a world-class team of inference engineers. Our current stack includes Python, PyTorch, Rust, C++, and Kubernetes.

• Lead and grow a high-performing team of AI inference engineers
• Develop APIs for AI inference used by both internal and external customers
• Architect and scale our inference infrastructure for reliability and efficiency

What you need

• 5+ years of engineering experience with 2+ years in a technical leadership or management role
• Deep experience with ML systems and inference frameworks (PyTorch, TensorFlow, ONNX, TensorRT, vLLM)
• Strong understanding of LLM architecture: Multi-Head Attention, Multi/Grouped-Query Attention, and common layers

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What you'll do

As an AI Inference Engineer at Perplexity, you will have the opportunity to work on large-scale deployment of machine learning models for real-time inference. You will be responsible for developing APIs for AI inference that will be used by both internal and external customers.

• Develop APIs for AI inference that will be used by both internal and external customers
• Benchmark and address bottlenecks throughout our inference stack
• Improve the reliability and observability of our systems and respond to system outages
• Explore novel research and implement LLM inference optimisations

What you need

To be successful in this role, you will need to have experience with ML systems and deep learning frameworks (e.g. PyTorch, TensorFlow, ONNX), familiarity with common LLM architectures and inference optimisation techniques (e.g. continuous batching, quantisation, etc.), and understanding of GPU architectures or experience with GPU kernel programming using CUDA.

• Experience with ML systems and deep learning frameworks (e.g. PyTorch, TensorFlow, ONNX)
• Familiarity with common LLM architectures and inference optimisation techniques (e.g. continuous batching, quantisation, etc.)
• Understanding of GPU architectures or experience with GPU kernel programming using CUDA

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