Research Scientist

3 нед. назад

250k–450k USD / годСША

machine learningdata analysispython

Research Scientist at Labs focusing on AI model benchmarking and dataset evaluation to advance AI capabilities.

is building the visual cortex for AGI.
Every day, millions of developers and half of the Fortune 100 use to count cells to accelerate cancer research, improve construction site safety , digitize floor plans , preserve coral reef populations , guide drone flight , and much more .
We have built the tools, community, and resources needed to make the world programmable with artificial intelligence. We're supported by great customers and investors, having raised over $100 million from Google Ventures, Craft Ventures, Sam Altman, Lachy Groom, Y Combinator, and more.
Labs is our applied research unit. We train our own open models (RF-DETR). We build our own benchmarks grounded in real enterprise use cases (RF100-VL). And we partner with frontier labs to deliver the training data they need to advance the capabilities that drive the most value back to our customers.
Behind all of it is a global network of millions of developers who have annotated billions of images and videos to date, as well as a proven enterprise business that is exploding as AI traverses the physical economy.

As a Research Scientist at Labs, you'll sit at the intersection of our benchmarks, our models, and the researchers at frontier labs who buy our data: running failure analyses that show exactly where a model breaks on real-world tasks, turning those findings into dataset and evaluation specifications, and validating that the data we ship actually moves the numbers. You'll publish, extend RF100-VL and the benchmarks that follow it, and help set the research agenda for a team whose work is grounded in millions of real deployments rather than fabricated test sets.
Design datasets, evaluations, and RL environments that target measurable gaps in frontier vision-language, multi-modal, robotics, and coding models
Run structured failure analyses on partner labs' models against 's real-world task distribution and write the remediation specs that say what data will close each gap
Own the research behind new benchmarks that we publish.
Validate data quality empirically — fine-tune, ablate, and measure so that every dataset we sell comes with evidence it works
Contribute to 's open models (RF-DETR and successors) and represent Labs within the research community
Work directly with research scientists at frontier labs as a technical peer, translating their model roadmaps into concrete data programs

You are a researcher who wants to see your work turn into data that ships. You have high agency and a bias toward action.
PhD or equivalent research experience in computer vision, multi-modal learning, machine learning, or a closely related field, with a track record of published work or shipped research
Hands-on experience with post-training: fine-tuning, RL, evaluation design, or data curation for large models
Strong engineering fundamentals — you write the code for your own experiments and can hand it to an engineer to productionize
Clear written communication; you can explain a failure mode to a lab researcher and a data spec to an annotator
Nice to have: experience at a frontier lab or as a data/evaluation partner to one; experience with vision-language models, robotics learning, or agentic coding evaluations

is a fully distributed company with hubs in San Francisco, New York City, Singapore, and Brazil. Labs works in person in San Francisco. We host full company retreats twice per year.
The target compensation for this role is $250,000 – $450,000 base salary commensurate with experience as well as equity in the company. Total cash comp is expected to scale significantly with growth of Labs.
In addition, we offer generous perks and benefits. Below are some of the highlights:
Generous stipends covering travel, productivity, tools, meals, and office.
Up to 100% of your health insurance costs covered.
Unlimited PTO - with an annual 2 week minimum.
12 weeks parental leave
We want a diverse, global team with a broad range of experience and perspectives. We carefully consider every application and will either move forward with you, find another team that might be a better fit, keep in touch for future opportunities, or thank you for your time.