- Company Name
- Weights & Biases
- Job Title
- AI Engineer- Gen AI/SWE- Weights & Biases
- Job Description
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Job Title: AI Engineer – Gen AI / SWE (Weights & Biases)
Role Summary: Design, ship, and maintain end‑to‑end generative AI workflows, including prompting, retrieval‑augmented generation, agentic tool use, evaluation, and production deployment, while ensuring reproducibility, safety, and performance.
Expectations:
- 6+ years of building production software systems.
- Proven track record delivering LLM‑powered features with measurable impact.
- Strong expertise in responsible AI deployment and reproducible research practices.
Key Responsibilities:
- Develop end‑to‑end GenAI pipelines (prompting → RAG → agents → evaluation → serving).
- Build agentic systems with secure tool integration, function‐calling, and multi‑step planners.
- Design evaluation harnesses for RAG/agent performance, including golden sets and regression tests.
- Publish reusable code, documentation, tutorials, and public presentations.
- Partner with product and solutions teams to deliver LLM features with clear latency, cost, and safety targets.
- Conduct growth experiments and analyze usage metrics of deployed artifacts.
Required Skills:
- Python or TypeScript (lead language) with strong system design, testing, CI/CD, and observability skills.
- Experience shipping LLM applications (tools, agents, function‑calling) at production scale.
- Proficiency in agentic patterns—planners, executors, sandboxing, and failure taxonomy.
- Expertise in RAG: chunking, embeddings, vector database design, and retrieval policy.
- Evaluation design: offline golden sets, counterfactuals, user studies, guardrail tests; statistical literacy (variance, CI, power).
- Serving & productization: queueing, caching, streaming, cost control, latency troubleshooting.
- Public signal: ≥2 major OSS projects, blog posts, talks, or videos with significant adoption (stars, forks, views).
- Familiarity with AI SDKs/frameworks, agent frameworks, and developer‑facing examples.
Required Education & Certifications:
- Bachelor’s degree (or higher) in Computer Science, Software Engineering, or related field (or equivalent professional experience).
- Relevant certifications in AI/ML, cloud platforms, or data engineering are a plus.