
Staff AI Platform Engineer
Laurel
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • New York • United States
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Salary
💰 $200,000 - $250,000 per year
Job Level
Tech Stack
About the role
- Own business‑critical AI challenges. Partner with product, design, and engineers to uncover the real customer problems, then frame them as tractable projects supported by a AI platform.
- Build end‑to‑end solutions. Harden and build out our AI platform to be world class.
- Ship incrementally, learn rapidly. Break ambitious ideas into testable slices, measure impact, and iterate.
- Elevate the team. Mentor engineers on best practices in AI engineering, model evaluation, prompt design, and responsible AI.
- Take true ownership. We empower every team member to understand the business levers behind their work and to push for outcomes.
Requirements
- Deep experience building AI/ML platforms at scale (REQUIRED)
- Strong backend / distributed systems fundamentals (REQUIRED)
- Hands-on experience with LLMs in production (REQUIRED)
- Experience designing AI infrastructure, not just models (REQUIRED)
- Proven ability to operate in fast-moving, ambiguous environments (REQUIRED)
- Experience mentoring and raising the bar (REQUIRED)
- Ownership mindset (REQUIRED)
- Nice to haves: Experience building agentic systems or multi-step LLM workflows, Experience with RAG architectures at scale, Familiarity with model evaluation frameworks, Experience optimizing LLM cost/latency tradeoffs, Exposure to data infrastructure, Experience with vector databases and search systems, Familiarity with AI safety, alignment, or compliance considerations, Experience working in high-growth startup environments, Background in legal tech, productivity tools, or enterprise SaaS
Benefits
- Great employee benefits, including equity and 401K
- Bi-annual, in-person company off-sites, in unique locations, to grow and share time with the team
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
AI platformsML platformsbackend systemsdistributed systemsLLMsAI infrastructuremodel evaluationRAG architecturesvector databasessearch systems
Soft Skills
mentoringownership mindsetoperating in ambiguous environmentsteam elevationbest practices in AI engineering