Staff Machine Learning Engineer, Virtual Collaborator at ANTHROPIC PBC DBA ANTHROPIC INC

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

02 Feb, 26

Salary

425000.0

Posted On

04 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Python Programming, Machine Learning, Reinforcement Learning, Data Creation, Document Manipulation, Collaboration, Evaluation Systems, Crowdsourcing, Enterprise Tools, APIs, Data Pipelines, Quality Control, Reward Modeling, Technical Training Objectives

Industry

Research Services

Description

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. Annual Salary: $340,000—$425,000 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. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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Responsibilities

Design and implement reinforcement learning pipelines for virtual collaborator use cases. Build and scale data creation platforms and integrate real organizational data for training environments.