Job Details

Staff Machine Learning Engineer, Virtual Collaborator

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 looking for a Machine Learning Engineer to help us train Claude specifically for virtual collaborator workflows. While Claude excels at general tasks, a lot of knowledge work requires targeted training on real organizational data and workflows. Your job will be to design and implement reinforcement learning environments that transform Claude into the best virtual collaborator, training on everything from navigating internal knowledge to creating financial models.

Responsibilities

• Designing and implementing reinforcement learning pipelines specifically targeted at virtual collaborator use cases (productivity, organizational navigation, vertical domains)
• Building and scaling our data creation platform for generating high-quality, open-ended tasks with domain experts and crowdworkers; integrating real organizational data to create authentic training environments
• Developing robust rubric-based evaluation systems that maintain quality while avoiding reward hacking
• Training Claude on advanced document manipulation, including understanding, enhancing, and co-creating
• Partnering directly with product teams to ensure training aligns with shipped features

You may be a good fit if you:

• Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using
• Have strong machine learning experience
• Thrive at the intersection of research and product, with a pragmatic approach to solving real-world problems
• Are comfortable with ambiguity and can balance research rigor with shipping deadlines
• Enjoy collaborating across multiple teams (data operations, model training, product)
• Can context-switch between research problems and product engineering tasks
• Care about making AI genuinely helpful for everyday enterprise workflows

Strong candidates may also have experience with:

• Building human-in-the-loop training systems or crowdsourcing platforms
• Working with enterprise tools and APIs (Google Workspace, Microsoft Office, Slack, etc.)
• Developing evaluation frameworks for open-ended tasks
• Domain expertise in finance, legal, or healthcare workflows
• Creating scalable data pipelines with quality control mechanisms
• Reward modeling and preventing reward hacking in RL systems
• Translating product requirements into technical training objectives

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The expected base compensation for this position is below. Our total compensation package for full‑time employees includes equity, benefits, and may include incentive compensation.

$340,000 - $560,000 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. 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 people who identify as being from under‑represented 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. 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 working 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.

Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Equal Employment Opportunity

As set forth in Anthropic's Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows: a "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service‑connected disability. A "recently separated veteran" means any veteran during the three‑year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service. An "active duty wartime or campaign badge veteran" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense. An "armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.