Applied GenAI Research Engineer
Applied GenAI Research Engineer
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$ 250000 - 350000
1 Vacancy
Job Description
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: $250000 $350000 (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
Employment Type
Full Time
