Research Engineer, Visual Knowledge Work

Anthropic; Seattle (WA); Híbrido; USD 350 000 - 850 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Competitive compensation

Equity donation matching

Generous vacation

Parental leave

Flexible working hours

Lovely office space

Resumo da oferta

Anthropic is seeking a Research Engineer for the Vision team to own end-to-end data strategy for vision capabilities within LLMs. You will design, evaluate, and scale RL environments and data pipelines to advance multimodal reasoning and knowledge work.

You will also manage external data vendors, refine reward design, and collaborate with pretraining, RL, and product teams to ensure environments translate to real-world capabilities. A highly cross-functional role with significant impact.

Qualificações

• 7+ years of ML, computer vision and software engineering experience.
• Experience with reinforcement learning, reward design, or data curation for large language or vision-language models.
• Familiar with the architecture, training, and operation of large vision-language models.
• Capable of managing technical vendor relationships and iterating quickly on feedback.
• Results-oriented with a bias towards flexibility and impact.

Responsabilidades

• Own the data strategy for vision capabilities end-to-end, including building evals and scaling RL environments.
• Manage technical relationships with external data vendors; write task specs and evaluate visual data quality.
• Develop and improve QA frameworks to catch reward hacking and ensure environment quality at scale.
• Run generalization experiments to measure data strategy impact on multimodal capabilities.
• Collaborate with pretraining, RL, and product teams to align efforts across disciplines.

Conhecimentos

ML experience

Computer vision

Software engineering

Reinforcement learning

Reward design

LLM architectures

Vendor management

Formação académica

Bachelor’s degree or higher

Descrição da oferta de emprego

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're looking for a research engineer who believes that visual and spatial reasoning are core to fully unlocking the capabilities of LLMs. On the Vision team, you'll own the end-to-end process of creating training data and RL environments targeting visual knowledge work: identifying long-horizon and vision-heavy tasks, building evals, designing rewards, and scaling data. This is a unique role that combines applied research with hands-on data work. It's also highly collaborative — you'll partner with external vendors, pretraining, RL, and product teams to make sure the environments you built translate into real-world knowledge work capabilities.

What You'll Do

• Own the data strategy for vision capabilities end-to-end, from building evals and scaling RL environments
• Manage technical relationships with external data vendors, including writing task specifications, evaluating visual data and annotation quality, and iterating on reward design
• Develop and improve QA frameworks that catch reward hacking and ensure environment quality at scale
• Run generalization experiments to measure how data strategy changes improve multimodal capabilities on held-out evaluations
• Partner with pretraining, RL, and product teams, and do the science that shows we’re all rowing in the same direction

You May Be a Good Fit If You

• Have 7+ years of ML, computer vision, and software engineering experience through industry, academia, or other projects
• Have experience with reinforcement learning, reward design, or training data curation for large language or vision-language models
• Are familiar with the architecture, training, and operation of large vision language models
• Are comfortable managing technical vendor relationships and iterating quickly on feedback
• Are results-oriented, with a bias towards flexibility and impact
• Care about the societal impacts of your work

Strong Candidates May Also Have Experience With

• Designing evals or benchmarks for LLMs or vision language models
• Large-scale pretraining, SL, and RL on language models
• Deep learning research on images, video, or other modalities
• Developing complex agentic systems using LLMs
• Large-scale ETL and data pipeline development

Representative Projects

• Writing a vendor-facing specification for a new family of visual RL training tasks, then iterating with the vendor on coverage, quality, and reward design
• Running experiments to determine ideal training datamixes and parameters for a synthetically generated vision dataset
• Finetuning Claude to maximize its performance using a particular set of agent tools/skills

Annual Salary

$350,000—$850,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

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.

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.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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.