Our team is a specialized group of engineers operating at the intersection of technology and quantitative finance. We function as an internal centre of excellence, providing expert-level solutions, architecture, and hands-on development in AI, Cloud (AWS/GCP), DevOps, and high-performance computing.

As a forward deployed software engineer, you will be responsible for translating complex research requirements into robust, scalable, and secure technical architectures across on-prem, hybrid, and cloud environments. You will write high-quality, production-ready code across the full stack, including Python libraries, infrastructure-as-code (Terraform), CI/CD pipelines, automation scripts, and ML/AI proof-of-concepts.

You will also develop and maintain our suite of managed products, reusable patterns, and best practice guides to provide self-service options and accelerate onboarding for new and existing teams. Additionally, you will act as the primary technical point of contact for embedded engagements, owning projects from discovery and planning through to implementation, knowledge transfer, and support.

To succeed in this role, you will need to have a deep understanding of computer science principles, including data structures, algorithms, and system design. You will also need to have experience working with cloud providers, such as AWS or GCP, and be familiar with infrastructure-as-code concepts. Excellent verbal and written communication skills are also essential, as you will need to build strong relationships with stakeholders and articulate complex ideas to diverse audiences.

Innovative thinking and a passion for AI/ML and its practical applications are highly desirable. Experience designing systems and architectures from ambiguous business needs, as well as experience with scheduling or asynchronous workflow frameworks/services, is also preferred.

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As a Solutions Architect, you will be responsible for managing complex customer deployments across AWS, GCP, Azure, and on-prem environments. You’ll partner directly with customer engineering teams to provision and monitor services, debug and resolve infrastructure issues, and ensure performance and scalability using SRE best practices. This role blends hands-on technical problem-solving with customer-facing engagement, including technical discussions, demos, workshops, and enablement content creation. You’ll work closely with Sales Engineering, Field Engineering, Support, and Product to drive adoption and influence our product roadmap based on customer feedback.

We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love diving into infrastructure problems and solving them systematically
• You’re curious about how to scale complex ML systems in production environments
• You’re an expert in building and running containerized, distributed systems

We work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

The base salary ranges for this role is $180,000 to $200,000. 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).

We offer a variety of benefits to support your needs, including:

• Medical, dental, and vision insurance
• 100% paid for by CoreWeave
• Company-paid Life Insurance
• Voluntary supplemental life insurance
• Short and long-term disability insurance
• Flexible Spending Account
• Health Savings Account
• Tuition Reimbursement
• Ability to Participate in Employee Stock Purchase Program (ESPP)
• Mental Wellness Benefits through Spring Health
• Family-Forming support provided by Carrot
• Paid Parental Leave
• Flexible, full-service childcare support with Kinside
• 401(k) with a generous employer match
• Flexible PTO
• Catered lunch each day in our office and data center locations
• A casual work environment
• A work culture focused on innovative disruption

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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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You will have the opportunity to contribute directly to research projects yourself and have a direct impact on our ability to scale reward development across domains. You will work closely with researchers and translate ambiguous requirements into well-scoped engineering projects.

To be successful in this role, you should have prior research experience and be excited to work closely with researchers. You should have strong Python skills and experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms. You should be comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing tooling.

Strong candidates may also have experience with ML research, building internal tooling and platforms for ML researchers, data quality assessment and pipeline optimization, experiment tracking, evaluation frameworks, or MLOps tooling. They may also have experience with large-scale data processing, Kubernetes, distributed systems, or cloud infrastructure, and familiarity with reinforcement learning or fine-tuning workflows.

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About the team: The team includes Backend Engineers, Full-stack Developers, ML Engineers, Data Scientists, and Frontend Developers—collaborating to deliver intelligent and scalable AI experiences.

Responsibilities:

• Lead and mentor a team of backend and frontend developers, fostering a high-performing, collaborative engineering culture.
• Own the technical direction, architecture, and strategy for LLM, AI Agent, and ML initiatives.
• Architect and build AI Agents, RAG pipelines (including multimodal RAG), LLM-based services, and evaluation frameworks.
• Design, build, and deploy scalable AI/ML systems from concept to production.
• Collaborate closely with PMs, ML Engineers, and Data Scientists to convert business requirements into AI-driven solutions.
• Oversee full SDLC for your squad—ensuring strong engineering practices around coding, testing, quality, and reliability.
• Conduct detailed technical reviews and provide guidance to maintain code quality.
• Troubleshoot performance, scalability, and reliability issues in production AI systems.
• Champion continuous improvement, automation, and innovation within the team.
• Keep the team up-to-date with advances in LLMs, LLMOps, fine-tuning techniques, vector search, and generative AI.

Requirements:

• 8–10 years of experience in software development.
• More than 5 years of hands-on experience in AI/ML.
• More than 2 years in an engineering leadership role, preferably leading a cross-functional team.
• Hands-on experience building:
  • AI Agents / Agentic workflows
  • LLM pipelines (inference, orchestration, guardrails)
  • RAG systems (vector search, knowledge bases, multimodal RAG)
  • LLM fine-tuning (SFT, LoRA, instruction tuning)
  • LLMOps workflows (monitoring, evaluation, optimization, versioning)
• Strong understanding of ML algorithms, embeddings, transformers, vector semantics, and distributed systems.
• Experience with Vector Databases such as Elasticsearch, Weaviate, Pinecone, or Milvus.
• Proficiency with ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps tooling.

Good to have:

• Solid foundation in backend technologies such as Python, Java, Go, Node.js, and experience with Kafka.
• Knowledge of the Gaming industry or Customer Support domain.
• Familiarity with frontend frameworks (React, Angular, Vue.js).
• Exposure to functional programming (Clojure preferred; our team uses Clojure).
• Experience in startup/high-growth environments.
• Open-source contributions in AI/ML/LLM projects.
• Experience with containerization and orchestration (Docker, Kubernetes).

Bonus Points:

• Experience building production-grade LLM-powered automation for customer support or gaming.
• Publications, presentations, or contributions related to LLMs, RAG, or AI Agents.

Benefits:

• Hybrid setup
• Worker's insurance
• Paid Time Offs
• Other employee benefits to be discussed by our Talent Acquisition team in India.

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You will deeply understand the research workflows of our Finetuning teams and automate the high-friction parts – turning days of manual experimentation into hours. You’ll build the tools and infrastructure that enable researchers across the organisation to develop, evaluate, and optimise reward signals for training our models. Your scalable platforms will make it easy to experiment with different reward methodologies, assess their robustness, and iterate rapidly on improvements to help the rest of Anthropic train our reward models.

Responsibilities

• Design and build infrastructure that enables researchers to rapidly iterate on reward signals, including tools for rubric development, human feedback data analysis, and reward robustness evaluation
• Develop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologies
• Create tooling that allows researchers to easily compare different reward methodologies (preference models, rubrics, programmatic rewards) and understand their effects
• Build pipelines and workflows that reduce toil in reward development, from dataset preparation to evaluation to deployment
• Implement monitoring and observability systems to track reward signal quality and surface issues during training runs
• Collaborate with researchers to translate science requirements into platform capabilities
• Optimise existing systems for performance, reliability, and ease of use
• Contribute to the development of best practices and documentation for reward development workflows

You may be a good fit if you

• Have prior research experience
• Are excited to work closely with researchers and translate ambiguous requirements into well-scoped engineering projects
• Have strong Python skills
• Have experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms
• Are comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing tooling
• Can balance building robust, maintainable systems with the need to move quickly in a research environment
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Care about the societal impacts of your work and are motivated by Anthropic's mission to develop safe AI

Strong candidates may also have experience with

• Experience with ML research
• Building internal tooling and platforms for ML researchers
• Data quality assessment and pipeline optimisation
• Experiment tracking, evaluation frameworks, or MLOps tooling
• Large-scale data processing (e.g., Spark, Hive, or similar)
• Kubernetes, distributed systems, or cloud infrastructure
• Familiarity with reinforcement learning or fine-tuning workflows

Representative projects

• Building infrastructure that allows researchers to rapidly test new rubric designs against small models before scaling up
• Developing automated systems to detect reward hacks and surface problematic behaviours during training
• Creating tooling for comparing different grading methodologies and understanding their effects on model behaviour
• Building a data quality flywheel that helps researchers identify problematic transcripts and feed improvements back into the system
• Developing dashboards and monitoring systems that give researchers visibility into reward signal quality across training runs
• Streamlining dataset preparation workflows to reduce latency and operational overhead

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 the process.

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