Anthropic Fellows Program — ML Systems & Performance · Anthropic

Fri, 24 Apr 2026 11:26:22 GMT
Engineering

Anthropic · London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA · $3,850 USD / £2,310 GBP / $4,300 CAD per week · remote

Apply for the Anthropic Fellows Program , ML Systems & Performance. This four-month full-time research program provides funding and mentorship to promising technical talent. Fellows will work on an empirical project aligned with Anthropic's research priorities, with the goal of producing a public output. The program includes direct mentorship from Anthropic researchers, access to a shared workspace, connection to the broader AI safety and security research community, and a weekly stipend of $3,850 USD / £2,310 GBP / $4,300 CAD + benefits.

The program is designed to foster AI research and engineering talent. Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on their projects. In one of our earlier cohorts, over 80% of fellows produced papers.

We run multiple cohorts of Fellows each year and review applications on a rolling basis. This application is for cohorts starting in July 2026 and beyond.

To be eligible, you must be fluent in Python programming, available to work full-time on the Fellows program, and have a strong technical background in computer science, mathematics, or physics.

The program is open to individuals from underrepresented groups and encourages applicants to apply even if they don't meet every qualification.

Strong candidates may also have a strong background in a discipline relevant to a specific Fellows workstream (e.g. economics, social sciences, or cybersecurity) and experience in areas of research or engineering related to their workstream.

ML Systems & Performance Fellows will work on projects such as building a CPU simulator for accelerator workloads, adding backends for different accelerators on an open source project, building on demand infrastructure for other infrastructure heavy fellows projects, and building complex synthetic data or environment pipelines.

The program is a great opportunity for individuals who are motivated by making sure AI is safe and beneficial for society as a whole, excited to transition into empirical AI research, and thrive in fast-paced, collaborative environments.