Applied ML Engineer

Location: San Francisco, CA, in-office
Salary: $200K - $350K + 0.25% - 1% equity
Industry: AI / Research

Who You'll Join

A pre-seed AI startup founded two months ago, building a taste layer for AI models. You'll work on teaching models what great looks like across subjective domains: design, writing, personality, and visual style. The team is tiny (currently three people) and well-funded, with clear paths to revenue through partnerships with AI labs and a seed round planned soon.

What You'll Do

• Train reward models, classifiers, and verifiers that judge subjective qualities like design and writing style.
• Design and run frontier evaluations and benchmarks for domains where human taste matters.
• Run post-training experiments on open-source models to test new data formats and techniques like RLHF and DPO.
• Collaborate directly with AI labs and creative experts to design pilots and validate new approaches.
• Own full-stack research pipelines: experiments, training, data, evaluation, and results.
• Publish blogs and whitepapers on your findings.

Who You Are

• Two or more years training models in production or research settings, especially post-training and fine-tuning work.
• Hands-on experience with LLMs and diffusion or multimodal models. Pure classical ML is not enough.
• Comfortable moving fast through ambiguity. You think like a researcher but ship like an engineer.
• Excited about the problem itself: you care about taste, design, creativity, and reducing AI slop.
• Experience at startups or data companies (Scale, Snorkel, etc.). Big tech or purely academic backgrounds rarely adapt here.
• Bonus: Personal passion for a creative field like writing, design, art, or fashion strengthens your fit.

Tech Stack

Python, PyTorch, LLMs, image models, multimodal models, RLHF, DPO, video models, post-training techniques, data annotation tools.

Why You'll Thrive

• You join at the inflection point. The team is tiny, equity is meaningful, and you shape the research direction entirely.
• The problem is intellectually novel. Most AI labs ignore subjective domains. You're solving that.
• Culture is collaborative and low-ego. Founders came from search and design backgrounds, not cutthroat big tech. A taste stipend covers museums and design courses.
• Work is fast-moving but not toxic. Cycles range from one week for quick post-training runs to quarterly for deeper research, with frequent publishing and customer feedback.
• Clear growth path. Two engineers hired, seed round coming soon, and plans to hire four more roles this year.