Highlightai
Member of Technical Staff, ML
San Francisco office · Staff+
Sponsorship not specifiedDetected 183 days ago
Vector DatabasesMachine LearningPyTorchData EngineeringLLMsRAGMLOpsCommunication
About the role
- You will work across the full ML stack: data pipelines, model training, retrieval, ranking, evals, and deployment.
- You will ship features that make Highlight feel like magic.
- You will thrive here if you love shipping production ML systems, obsess over output quality, and want to work on hard problems with real user impact.
Responsibilities
- Highlight is building a shared intelligence layer for the modern workforce.
- We're hiring a Member of Technical Staff to help build the AI systems that power Highlight.
- This is a hands on IC role where you will own critical systems end to end and collaborate closely with our Head of AI Engineering to scale our ML capabilities.
- Design, build, and optimize ML pipelines for context retrieval, action detection, and output generation
- Implement and iterate on RAG systems, ranking models, and prompt engineering to improve quality
- Build comprehensive evals and monitoring to measure and improve ML system performance
- Collaborate with Backend and Product Engineering to ship AI powered features
Requirements
- 4+ years in ML/AI engineering, with significant experience shipping production systems
- Deep experience with retrieval, ranking, RAG, or search systems at scale
- Hands on proficiency with PyTorch, vector databases, and modern ML infrastructure
- Experience with LLM applications (prompt engineering, fine tuning, eval frameworks)
- You are a good fit if you have:
Compensation
- Competitive salary and generous equity package
Benefits
- Competitive salary and generous equity package
- Health, dental, and vision insurance
- Flexible PTO and parental leave
- Must be based in or willing to relocate to SF - although we're flexible with days off & schedules, we have a 100% in-office culture during the week.
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This listing is sourced directly from Highlightai's careers page and normalized into a canonical job model.