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

We are seeking a Machine Learning Systems Engineer to join our Model Evaluations team at Anthropic. This team is responsible for our Model Evaluations infrastructure and the APIs and infrastructure tailored for "Research Inference." In this role, you will build scalable systems that enable our researchers to effectively evaluate models and conduct inference tasks critical to our research mission. You'll collaborate with researchers across Anthropic to understand their needs and build infrastructure that makes their workflows more efficient and reproducible. Your work will directly impact Anthropic's ability to advance the frontiers of AI in a safe and responsible manner.

Responsibilities

Design, build, and maintain Model Evaluations infrastructure that enables researchers to systematically test and assess model capabilities
Develop and optimize APIs and infrastructure for Research Inference to accelerate the model development lifecycle
Create scalable data pipelines for collecting, processing, and analyzing research outputs
Implement monitoring, logging, and performance optimization for research-focused inference systems
Build intuitive interfaces and tools that allow researchers to configure, run, and analyze complex evaluation workflows
Collaborate with research teams to understand their evolving needs and translate requirements into reliable technical solutions
Improve system performance, reliability, and scalability to handle increasingly complex research needs
Participate in your team's on-call rotation, deliver operationally ready code, and exercise a high degree of customer focus in your work
Document systems thoroughly to enable broader adoption and ease of use

You May Be a Good Fit If You

Have 5+ years of software engineering experience
Have significant software engineering experience. If you’re a strong engineer with no ML experience, that’s okay!
Are results-oriented, with a bias towards flexibility and impact
Have experience with data infrastructure and processing large datasets
Are comfortable working independently and taking ownership of projects from conception to delivery
Have excellent communication skills and can collaborate effectively with research teams
Are proficient in Python and have experience with cloud infrastructure (AWS, GCP)
Can anticipate the needs of research users and design systems that are both powerful and usable
Pick up slack, even if it goes outside your job description
Enjoy pair programming (we love to pair!)
Care about the societal impacts of your work and are committed to developing AI responsibly

Strong Candidates May Also Have Experience With

High performance, large-scale ML systems
GPUs, Kubernetes, PyTorch, or ML acceleration hardware
Building evaluation frameworks for machine learning models
Working in or adjacent to ML research teams
Distributed systems design and optimization
Real-time inference systems for large language models
Performance profiling and optimization
Infrastructure as Code and CI/CD pipelines

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

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. 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.

Machine Learning Systems Engineer, Model Evaluations