Applied GenAI Research Engineer Location: San Francisco Bay Area (Hybrid 2 days/week onsite) Position Type: Full-Time (Permanent) Hiring Urgency: Urgent Start Date: ASAP Team Size: High-impact, research-driven engineering team About A deeply technical role driving frontier LLM infrastructure. This position focuses on building feedback-driven LLM training systems, evaluation pipelines, and tooling for human-in-the-loop AI developmentserving top-tier AI labs like Apple, Meta, and Google. Why join Collaborate with elite engineers and researchers at the frontier of GenAI Work on meaningful, production-grade LLM infrastructure High agency, startup culture with tangible impact Rapid interview-to-offer cycle (~12 days) Compensation Base Salary: $250,000 $350,000 (up to $375K for top-tier candidates) Equity: Standard 4-year vesting with 1-year cliff Visa Support: H1B transfers only (no new sponsorships) Sign-On Bonus: Available Tech environment Languages: Python Frameworks: PyTorch, JAX, TensorFlow AI Domains: LLMs, RLHF, DPO, multimodal models, alignment Tooling: Open-source ML stack, preference-based model tuning System Design: Large-scale ML training/eval/search pipelines Responsibilities Develop alignment techniques using RLHF, DPO, preference learning Build AI-assisted data labeling tools using active learning and adaptive sampling Implement scalable systems to assess human data and expert quality Solve optimization problems for expert-task matching at large scale Ship production and research-grade ML training infrastructure Collaborate cross-functionally with engineers, researchers, and clients Publish research, blog posts, and documentation to drive thought leadership Must-have qualifications Proven experience shipping LLM-related projects (fine-tuning, eval, retrieval, post-training, etc.) 3+ years in applied ML roles with measurable product impact Strong Python + DL framework experience (PyTorch, JAX, TensorFlow) Deep understanding of frontier AI models (LLMs, multimodal, alignment) Degree in CS, AI, ML, or related field (PhD or MS preferred) Experience building scalable ML systems (search, evaluation, training pipelines) High agency and startup mindset: independently driven, fast executor, problem-solver Preferred qualifications Contributions to open-source ML tooling or publications (e.g., NeurIPS, ICML, ICLR, ACL) Experience optimizing task-matching with expert networks Hands-on work with RLHF, DPO, or human-feedback-based learning Projects combining research and real-world ML engineering