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 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.

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