Staff AI Engineer

Responsibilities

Shape the Architecture

• Design and own production AI systems end-to-end (LLM pipelines, RAG, reranking, vector stores, orchestration).
• Make thoughtful build/don't-build decisions based on real data.
• Evolve our AI stack over time, from model infrastructure to workflow orchestration to evaluation tooling.

Raise the Quality Bar

• Design evaluation systems that measure retrieval quality, reasoning accuracy, end-to-end performance.
• Build tooling that helps the team iterate confidently and catch regressions early.
• Define how we measure success across the platform.

Investigate Deeply & Decide with Evidence

• Analyze production outputs to identify failure patterns and root causes.
• Turn complex findings into clear technical recommendations.
• Know when to push forward and when to pause based on data.

Lead Across Teams

• Be the go-to technical voice for AI architecture decisions.
• Influence standards for how LLM systems are built, tested, deployed, and monitored.
• Mentor senior engineers through design reviews and hands-on collaboration.
• Partner with product and compliance teams to translate domain complexity into clean technical solutions.

Build Responsible, Production-Ready AI

• Ship systems optimized for latency, cost, reliability, and auditability.
• Embed safety guardrails, confidence thresholds, and human-in-the-loop workflows.
• Ensure outputs are traceable and explainable.

Requirements

• 10+ years of software engineering experience, including 3+ years working directly on ML/AI systems.
• Real ownership of production LLM systems.
• Deep experience with RAG, embeddings, reranking, vector databases (Pinecone, FAISS, Chroma, etc.), and agentic workflows.
• Experience designing evaluation frameworks and using quantitative analysis to improve system performance.
• Strong Python skills (TypeScript is a plus).
• A track record of making architectural decisions that shape team direction.
• Experience operating AI systems in production - observability, reliability, cost tradeoffs.
• The ability to break down ambiguous, high-stakes problems into structured investigations.
• Clear communication skills and comfort working cross-functionally.

Nice to Have

• Experience in compliance, security, or other regulated domains.
• Familiarity with enterprise data platforms or Snowflake-based analytics.
• Experience with orchestration systems like Temporal or Airflow.
• Experience building or using LLM evaluation platforms (e.g., Braintrust).
• Contributions to technical communities or published work.

Compensation

• Competitive base salary, benefits, and stock (RSUs).
• Salary range: $200,700 - $271,500 (varies by tier: Tier 1 $220,800-$298,800; Tier 2 $198,700-$268,900; Tier 3 $176,600+).