Research Engineer / Research Scientist, Vision
AnthropicNew York City, NY; San Francisco, CA; Seattle, WAPosted 7 April 2026
Job 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’re looking for research engineers with a strong computer vision background who believe that visual and spatial reasoning are core to fully unlocking the capabilities of LLMs. In this role, you'll work on research, development, and evaluation for state-of-the-art Claude models, with a focus on visual and spatial capabilities. This role is highly collaborative and will touch many aspects of our broader research efforts, taking a full-stack approach across pretraining, RL, and runtime techniques like agentic harnesses. Additionally, you’ll partner with the product org to ensure that the vision improvements you deliver impact Claude’s performance on real-world tasks.
What you'll do:
Run experiments to evaluate architectural variants, data strategies, and SL and RL techniques to improve Claude’s vision
Develop and test tools, skills, and agentic infrastructure that enable Claude to reason over visual inputs
Create evaluations and benchmarks that measure progress on multimodal capabilities across training and deployment
Work with our product org to find solutions to our most vexing API customer challenges related to vision and spatial reasoning
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
Are familiar with the architecture, training, and operation of large vision language models
Have experience creating and evaluating large synthetic and real-world visual training datasets
Have experience engaging in systematic prompting, finetuning, or evaluation
Are results-oriented, with a bias towards flexibility and impact
Enjoy pair programming and cross-team collaboration
Care about the societal impacts of your work
Strong candidates may also have experience with:
Large-scale pretraining, SL, and RL on language models
Deep learning research on images, video, or other modalities
Developing complex agentic systems using LLMs
High-performance ML systems (GPUs, TPUs, JAX, PyTorch)
Large-scale ETL and data pipeline development
Representative projects:
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
Building a pipeline to ingest and process a novel source of visual training data
Designing and running experiments to evaluate the scalability of two architectural variants
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
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.
We encourage you to apply even ... (truncated, view full listing at source)
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