Source description
About the role
Software should be beautiful.
Variant is code generation with creativity and taste. Instead of a confined conversation, you can generate endless designs from a single idea. Freely explore, discover directions you wouldn’t have thought of, and build better software as a result.
We're well-capitalized and looking for an applied researcher to join us at Variant.
Job Responsibilities:
Design, train, and evaluate deep neural networks and LLMs
Own experiments end-to-end: hypotheses, datasets, metrics, results
Prototype fast; turn promising ideas into reliable systems
Collaborate with engineering and design to make ideas real
Write clearly: papers, docs, and crisp experiment reports
Don't compromise between rigor and shipping fast
See what needs to be done and do it
Minimal qualifications:
2+ years in PhD program or in industry
Experience training deep neural networks
Proficiency in frameworks like Pytorch
Published 1 or more papers in the subject of deep learning
Ideal qualifications:
Experience training LLMs, including SFT, RL-based techniques, etc.
Experience in the domain of code generation
At least 1 paper accepted into a top conference
Proficiency in best software engineering practices
Repeated experience formulating, structuring, and answering open ended questions
Passionate about visual design or UI design; prior work in the space
We've kept our team small by design. Your code will matter from day one, and its impact will compound over time. This will be the most meaningful work of your career .
We work from a beautiful space in San Francisco where we dream up the future and make it real together. We’re well-capitalized and backed by notable investors.
If you've built things you're proud of, we'd love to see them.
More at Variant