Anthropic
Machine Learning Systems Engineer, Research Tools (Artifacts)
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 seek an experienced Engineer to join our Model Artifacts team empowering Research at Anthropic. This team builds libraries, tooling, and infrastructure to make Research workflows more productive, efficient, collaborative, and safe. You will design and develop scalable systems that empower Anthropic's researchers to effectively track, analyze, and leverage data throughout the ML lifecycle, from training to model evaluation. Working closely with our research organization, you'll build and maintain critical infrastructure that supports groundbreaking AI research while ensuring system reliability, performance, and usability. Your work will directly impact Anthropic's ability to advance the frontiers of AI in a safe and responsible manner.
Responsibilities
- Design, build, and improve data engineering systems for research workflows, including data tracking, caching, and analysis
- Enhance and maintain our core artifact systems and other research productivity tools
- Collaborate with researchers to understand their needs and build solutions that make their workflows more efficient and reproducible
- Develop and optimize data pipelines for collecting, processing, and analyzing research data
- Build and maintain backends, UIs, and APIs that allow researchers to explore and utilize experimental data effectively
- Improve system performance, reliability, and scalability to handle increasingly complex research needs
- Implement monitoring, testing, and documentation to ensure system reliability and ease of use
- Participate in your team's on-call rotation, deliver operationally ready code, and exercise a high degree of customer focus in your work
- Work collaboratively with other engineering teams to integrate research tools with broader company infrastructure
You May Be a Good Fit If You
- Have 5+ years of engineering experience with a strong focus on Machine Learning infrastructure or Data Engineering
- Have experience building and maintaining data pipelines and infrastructure
- Are proficient in Python and comfortable working in a Linux environment
- Have experience with distributed systems and cloud infrastructure
- Are familiar with ML workflows and the technical needs of ML research
- Can effectively communicate technical concepts to both technical and non-technical stakeholders
- Are results-oriented, with a bias towards flexibility and impact
- Pick up slack, even if it goes outside your job description
- Enjoy pair programming and debugging as a way to learn and teach
- Have a desire to make researchers more productive through both infrastructure/libraries and direct support
- Are committed to developing AI responsibly and safely
Strong Candidates May Also Have Experience With
- ML infrastructure development or ML platform engineering
- Big data technologies (e.g., BigTable, BigQuery, Spark)
- Containerization and orchestration tools (e.g., Docker, Kubernetes)
- Working directly with ML researchers or in an ML research organization
- Understanding of large language models and their training/evaluation pipelines
- Building caching systems or data versioning tools
Deadline to apply: None. Applications will be reviewed on a rolling basis.
The expected salary range for this position is:
Annual Salary:
$300,000—$405,000 USD
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. 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.
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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