Applied ML Engineer

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Job Description: Member of Technical Staff (Applied ML)

Location: San Francisco, CA, USA

About the Role
As a Machine Learning Research Engineer, you’ll drive research that teaches models what great feels like across domains such as model personality and behavior, UI design, multi-modal generation, and writing tone. It’s a hard, ambiguous, and (very) cool problem space.

You’ll own full-stack research: experiments, training runs, data and eval pipelines, and publishing results. You’ll develop internal research and proprietary models while collaborating directly with AI labs on frontier projects.

What You'll Do

• Train reward models, classifiers, and verifiers for subjective domains (e.g., design, writing, visual style).
• Develop frontier evaluations and benchmarks for subjective domains.
• Run post-training experiments on open-source models to test new data formats and post-training techniques.
• Collaborate with AI labs and creative experts to design pilots and experiments around taste.
• Own the end-to-end pipeline.
• Publish blogs and whitepapers.

You Might Be a Good Fit If You

• Are obsessed with taste and want a world with less AI slop.
• Have experience in ML research, Applied ML, or ML research engineering, especially in post-training/fine-tuning large models (SFT, RLHF, DPO). Experience with LLM/diffusion models is required.
• Think like a researcher, move like an engineer. Are creative, scrappy, and comfortable operating in ambiguity.

Tech Stack: Python, Pytorch, LLMs, Image Models, Multimodal Models, RLHF, DPO

Additional Details

• Visa sponsorship available
• On-site work policy
• Full-time position