AI Engineer- Gen AI/SWE- Weights & Biases

Weights & Biases•Sunnyvale, CA

5d•$188,000 - $275,000•Hybrid

About The Position

CoreWeave, the AI Hyperscaler™, acquired Weights & Biases to create the most powerful end-to-end platform to develop, deploy, and iterate AI faster. Since 2017, CoreWeave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME100 most influential companies of 2024. By bringing together CoreWeave’s industry-leading cloud infrastructure with the best-in-class tools AI practitioners know and love from Weights & Biases, we’re setting a new standard for how AI is built, trained, and scaled. The integration of our teams and technologies is accelerating our shared mission: to empower developers with the tools and infrastructure they need to push the boundaries of what AI can do. From experiment tracking and model optimization to high-performance training clusters, agent building, and inference at scale, we’re combining forces to serve the full AI lifecycle — all in one seamless platform. Weights & Biases has long been trusted by over 1,500 organizations — including AstraZeneca, Canva, Cohere, OpenAI, Meta, Snowflake, Square,Toyota, and Wayve — to build better models, AI agents and applications. Now, as part of CoreWeave, that impact is amplified across a broader ecosystem of AI innovators, researchers, and enterprises. As we unite under one vision, we’re looking for bold thinkers and agile builders who are excited to shape the future of AI alongside us. If you're passionate about solving complex problems at the intersection of software, hardware, and AI, there's never been a more exciting time to join our team. The AI team is a hands-on applied AI group at Weights & Biases that turns frontier research into teachable workflows. We collaborate with leading enterprises and the OSS community. We are the team that took W&B from a few hundred users to millions of users and one of the most beloved tools in the ML community. A senior applied role at the research-to-product boundary. You will design, implement, and evaluate LLM applications and agents with cutting-edge techniques from the latest research, then document and teach them to our community and customers. The focus is application, not novel research: rapid prototyping, careful evaluation, and production-grade reference implementations with clear trade-offs. We prioritize responsible, safe deployment and reproducibility.

Requirements

Software engineering: 6+ years building production systems; strong Python or TypeScript + system design, testing, CI/CD, observability.
GenAI apps: shipped LLM-powered features (tools/agents/function calling), with measurable impact (latency/cost/reliability).
Agentic patterns: implemented planners/executors, tool orchestration, sandboxing, and failure taxonomies; familiarity with agent infra concerns.
RAG: pragmatic mastery of chunking, embeddings, vector/hybrid search, rerankers; experience with vector DBs/search indices and retrieval policy design.
Evaluation: designed LLM/RAG/agent evals (offline golden sets, counterfactuals, user studies, guardrail tests); stats literacy (variance, CIs, power).
Serving & productization: comfortable with queueing, caching, streaming, and cost controls; can debug latency at model, retrieval, and network layers.
Public signal: 2+ substantial OSS repos/blog posts/talks/videos with adoption (stars, forks, downloads, views) and reproducible artifacts.

Nice To Haves

Experience building with AI SDKs / agent frameworks (e.g., TypeScript/Python SDKs, planning libraries) and shipping developer-facing examples.
Production agent security/sandboxing, red-teaming, and policy/PII enforcement.
Operated eval platforms or built judge models/heuristics; experience leading metrics reviews with product/UX.
Customer-facing enablement: templates or reference implementations adopted by external teams at scale.

Responsibilities

Ship end-to-end GenAI workflows (prompting → RAG → tools/agents → eval → serve) with reproducible repos, W&B Reports, and dashboards others can run.
Build agentic systems (tool use, function calling, multi-step planners) with MCP servers/clients and secure tool/resource integrations.
Design evaluation harnesses (RAG/agent evals, golden sets, regression tests, telemetry) and drive continuous improvement via offline + online metrics.
Build in public: Publish engineering artifacts (code, docs, talks, tutorials) and engage with OSS and customer engineers; turn repeated patterns into reusable templates.
Partner with product/solutions to launch LLM-powered features with clear latency/cost/SLO targets and safety/guardrail checks.
Run growth experiments to track the usage of the Weights & Biases suite of products from the artifacts built.