Machine Learning Engineer, Safeguards Research

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. 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 Safeguards Research Team conducts critical safety research and engineering to ensure AI systems can be deployed safely. As a Machine Learning Engineer on our team, you'll bridge the gap between research and engineering, developing robust end-to-end pipelines and ML systems that directly support our safety research initiatives. You'll work on building scalable infrastructure for evaluating safety systems, implementing efficient training pipelines for safeguards, and creating automated systems to help us understand and mitigate risks in advanced AI systems.

You bring both ML fundamentals and strong engineering practices to the team. You're comfortable training and fine-tuning models, have intuitions about hyperparameter optimization, and can implement efficient data processing pipelines. You take a pragmatic approach to ML engineering, preferring simple, effective solutions over complex ones. You'll collaborate closely with researchers to translate experimental concepts into production-quality ML systems that address both immediate safety challenges and support longer-term research initiatives.

While deep theoretical ML knowledge is beneficial, we value practical ML experience and the ability to implement reliable systems that improve research productivity.

Representative projects:

• Design and implement ML pipelines for training and evaluating safety classifiers and detection models
• Develop systems to fine-tune language models for specific safety evaluation tasks
• Build infrastructure for hyperparameter optimization and model selection across safety experiments
• Create efficient data processing pipelines that can handle large-scale model outputs and training datasets
• Develop tooling to automate the generation, analysis, and classification of jailbreak attempts
• Build evaluation frameworks that can systematically test model behaviors across safety dimensions
• Create flexible interfaces for researchers to experiment with different model architectures and training configurations

You may be a good fit if you:

• Have hands-on experience training and fine-tuning basic ML models
• Understand fundamental ML concepts like overfitting and regularization
• Have practical experience with improving and evaluating ML models
• Are proficient with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and can implement custom training loops
• Have strong software engineering skills, particularly with Python
• Excel at building scalable data pipelines and ML infrastructure
• Are experienced with prompting and working with large language models
• Prefer implementing simple, reliable solutions over complex ones
• Are comfortable working in a fast-paced, collaborative research environment
• Care deeply about the impacts of AI

Strong candidates may also:

• Have implemented custom loss functions and evaluation metrics
• Have experience with experiment and evaluation tracking tools
• Have built systems that integrate training, evaluation, and deployment pipelines
• Have contributed to open-source machine learning or AI safety tools

The expected salary range for this position is:

Annual Salary: $320,000 - $430,000 USD

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.

Note: Currently, the team has a strong preference for candidates who are able to be based in the Bay Area. However, we remain open to any candidate who can travel 25% to the Bay Area.

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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

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, flexible working hours, and a lovely office space in which to collaborate with colleagues.