You will work closely with different business lines across Anduril to help discover and scope new research problems. To be successful in this role, you will need a strong background in machine learning, experience developing and benchmarking ML algorithms, and strong Python skills with experience in ML frameworks such as PyTorch.

The salary range for this role is $220,000-$292,000 USD, and highly competitive equity grants are included in the majority of full-time offers. Additionally, Anduril offers top-tier benefits for full-time employees, including comprehensive medical, dental, and vision plans, income protection, generous time off, family planning and parenting support, mental health resources, professional development, commuter benefits, and relocation assistance.

If you're interested in this opportunity, please reach out to the recruiter assigned to this role to learn more about the specific compensation and benefit details associated with this role.

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Key responsibilities include designing, running, and analyzing scientific experiments to advance our understanding of large language models. Additionally, you will optimize and scale our training infrastructure to improve efficiency and reliability, and develop and improve dev tooling to enhance team productivity.

As a Research Engineer, you will contribute to the entire stack, from low-level optimizations to high-level model design. You will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

The ideal candidate will have an advanced degree in Computer Science, Machine Learning, or a related field, and strong software engineering skills with a proven track record of building complex systems. You should be familiar with Python and experience with deep learning frameworks, particularly PyTorch. Additionally, you should have expertise in large-scale machine learning, particularly in the context of language models.

You will thrive in this role if you have significant software engineering experience, are results-oriented with a bias towards flexibility and impact, willing to take on tasks outside your job description to support the team, enjoy pair programming and collaborative work, and are eager to learn more about machine learning research.

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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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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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At Cresta, we are on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioural best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster.

As a Senior Machine Learning Engineer, you will lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches. You will architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.

Responsibilities:

• Lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches.
• Architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.
• Design, deploy, and optimize LLM-powered systems, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration, and domain-adapted models.
• Improve reasoning, planning, and tool-use capabilities in real-world AI applications.
• Develop evaluation strategies for complex, non-deterministic systems, including offline benchmarking, online experimentation, and LLM-as-a-judge methodologies.
• Diagnose and mitigate real-world failure modes such as hallucinations, retrieval errors, tool misuse, prompt brittleness, and multi-step reasoning breakdowns.
• Define and measure quality metrics (e.g., accuracy, faithfulness, task completion, latency, cost, robustness) to improve system reliability and performance.
• Optimize AI systems for scalability, latency, security, and cost efficiency in production environments.
• Collaborate cross-functionally with product, frontend, and backend teams to integrate AI capabilities seamlessly into Cresta's platform.
• Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta's AI systems.

Qualifications:

• Bachelor's degree in Computer Science, Mathematics, or a related field; Master's or Ph.D. preferred.
• 5–8+ years of industry experience building and deploying machine learning systems in production, including significant experience working with LLMs.
• Strong expertise in NLP, Generative AI, transformer architectures, embeddings, and retrieval systems.
• Proven experience designing and deploying Retrieval-Augmented Generation (RAG) systems in enterprise environments.
• Experience building and evaluating complex agentic or multi-step LLM workflows.
• Strong knowledge of modern ML frameworks and tools (e.g., PyTorch, TensorFlow, Hugging Face) and distributed/cloud-based infrastructure.
• Demonstrated ability to optimize real-time ML systems for performance, scalability, and reliability.
• Strong technical leadership skills, with the ability to influence cross-functional decisions and raise the engineering bar.

Perks & Benefits:

• We offer Cresta employees a variety of medical, dental, and vision plans, designed to fit you and your family's needs.
• Paid parental leave to support you and your family.
• Monthly Health & Wellness allowance.
• Work from home office stipend to help you succeed in a remote environment.
• Lunch reimbursement for in-office employees.
• PTO: 3 weeks in Canada.

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Member of Technical Staff - Image / Video Generation

Job Description

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX,foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video,tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.

Why This Role

You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes,it's about running the experiments that clarify which architectural choices matter and which are less impactful.

What You’ll Work On

• Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters
• Rigorously ablates design choices,running experiments that isolate variables, control for confounds, and produce insights you can actually trust,then communicating those results to shape our research direction
• Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously
• Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough

What We’re Looking For

• You've trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You're comfortable debugging distributed training issues and presenting research findings to the team.

Required Skills

• Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training
• Experience fine-tuning diffusion models for specialized applications,upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture
• Deep understanding of how to effectively evaluate image and video generative models,knowing which metrics correlate with quality and which are just convenient proxies
• Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning
• Solid understanding of distributed training techniques,FSDP, low precision training, model parallelism,because our models don't fit on one GPU and training decisions impact research outcomes

Preferred Skills

• Experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors
• Proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers
• Know the performance characteristics of different architectural choices at scale
• Have published research that contributed to how people think about generative models

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

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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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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.

Key Responsibilities:

• Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development

• 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 and scale our training infrastructure to improve efficiency and reliability

• Develop and improve dev tooling to enhance team productivity

• Contribute to the entire stack, from low-level optimisations to high-level model design

Qualifications:

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field

• Strong software engineering skills with a proven track record of building complex systems

• Expertise in Python and experience with deep learning frameworks (PyTorch preferred)

• Familiarity with large-scale machine learning, particularly in the context of language models

• Ability to balance research goals with practical engineering constraints

• Strong problem-solving skills and a results-oriented mindset

• Excellent communication skills and ability to work in a collaborative environment

• Care about the societal impacts of your work

Preferred Experience:

• Work on high-performance, large-scale ML systems

• Familiarity with GPUs, Kubernetes, and OS internals

• Experience with language modelling using transformer architectures

• Knowledge of reinforcement learning techniques

• Background in large-scale ETL processes

You'll thrive in this role if you:

• Have significant software engineering experience

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

• Willingly take on tasks outside your job description to support the team

• Enjoy pair programming and collaborative work

• Are eager to learn more about machine learning research

• Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects

• Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research

• View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximise the impact of your insights

• Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.

Sample Projects:

• Optimising the throughput of novel attention mechanisms

• Comparing compute efficiency of different Transformer variants

• Preparing large-scale datasets for efficient model consumption

• Scaling distributed training jobs to thousands of GPUs

• Designing fault tolerance strategies for our training infrastructure

• Creating interactive visualisations of model internals, such as attention patterns

Benefits:

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

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 https://job-boards.greenhouse.io.

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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.

Key Responsibilities:

• Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
• 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 and scale our training infrastructure to improve efficiency and reliability
• Develop and improve dev tooling to enhance team productivity
• Contribute to the entire stack, from low-level optimisations to high-level model design

Qualifications:

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
• Strong software engineering skills with a proven track record of building complex systems
• Expertise in Python and experience with deep learning frameworks (PyTorch preferred)
• Familiarity with large-scale machine learning, particularly in the context of language models
• Ability to balance research goals with practical engineering constraints
• Strong problem-solving skills and a results-oriented mindset
• Excellent communication skills and ability to work in a collaborative environment
• Care about the societal impacts of your work

Preferred Experience:

• Work on high-performance, large-scale ML systems
• Familiarity with GPUs, Kubernetes, and OS internals
• Experience with language modelling using transformer architectures
• Knowledge of reinforcement learning techniques
• Background in large-scale ETL processes

You'll thrive in this role if you:

• Have significant software engineering experience
• Are results-oriented with a bias towards flexibility and impact
• Willingly take on tasks outside your job description to support the team
• Enjoy pair programming and collaborative work
• Are eager to learn more about machine learning research
• Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects
• Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
• View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximise the impact of your insights
• Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.

Sample Projects:

• Optimising the throughput of novel attention mechanisms
• Comparing compute efficiency of different Transformer variants
• Preparing large-scale datasets for efficient model consumption
• Scaling distributed training jobs to thousands of GPUs
• Designing fault tolerance strategies for our training infrastructure
• Creating interactive visualisations of model internals, such as attention patterns

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

If you're excited about pushing the boundaries of AI while prioritising safety and ethics, we want to hear from you!

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:

$350,000 - $850,000USD

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We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you'll build and scale the critical infrastructure that powers our AI safety systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale.

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

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 the state of the art in AI safety and making a meaningful difference in the world.

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