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