AI Engineer

CoreWeave•Sunnyvale, NY

44d•Onsite

About The Position

The Field Engineering organization at CoreWeave supports the clients running some of the largest AI workloads in the world. This team builds the tooling that engineers rely on to identify and diagnose customer issues faster. We work where AI and deterministic based systems meet live production infrastructure, so what we build has to be accurate, has to show its reasoning, and has to earn the trust of engineers who are the real experts solving issues. As an AI Engineer, you'll design, build, and operate systems that engineers use during real customer incidents. You will define the right balance between deterministic and AI systems, and work with both. For AI systems, you'll own the agent logic, the retrieval behind it, the safeguards that stop it from acting on bad information, and the measurement that tells us whether it's actually helping. This is a full-ownership role: you ship your own services and stay responsible for how they behave in production. You'll work closely with the engineers who use what you build.

Requirements

3+ years of professional software engineering experience, with strong proficiency in Go or Python.
Experience taking an AI application from prototype to production use and supporting it afterward.
Hands-on experience with agent patterns: tool and function calling, multi-step orchestration, and handling incorrect model output.
Experience building and evaluating retrieval systems, including measuring quality against a baseline using held-out data.
Experience designing controls for automation that acts on production systems, such as approval steps, idempotency, and audit trails.
Experience diagnosing failures in LLM applications and fixing the underlying cause rather than the symptom.
Experience with observability for LLM applications, including tracing, cost, and latency.
Working knowledge of Kubernetes and experience deploying services through CI/CD.
Experience integrating with third party APIs, including authentication and secrets handling.

Nice To Haves

Experience building conversational or chat-based interfaces for internal users.
A background in infrastructure, SRE, or technical support, and comfort reading logs and metrics from a live system.
Experience testing both deterministic and non-deterministic systems.
Experience with fine-tuning or building training datasets from production data.
Open source contributions to AI tooling or agent frameworks.
You can tell the difference between the thing that's interesting and the thing that's urgent, and you'll say so out loud when they aren't the same.
You've worked with a team that already had tooling they liked, and found the integration instead of arguing for a rewrite.
You'd rather sit in on a real incident than infer how the work goes from a ticket.
Blunt feedback from the engineers using your software is the most useful thing you'll hear all week, and you don't get defensive about it.

Responsibilities

Design and build AI agents that investigate and act on real customer issues.
Build the safeguards that make that safe: grounding model output in authoritative data, requiring human approval before consequential actions, and defining the conditions under which the system should stop.
Build and evaluate retrieval over a large body of historical support data, measured against how experienced engineers handled the same problems.
Determine the optimal balance between AI and deterministic logic.
Instrument these systems for quality, cost, and adoption, then use what you learn to decide what to build next.
Deploy and operate your own services on Kubernetes, including supporting them when something breaks.
Partner with support engineers and domain experts, including sitting in on live investigations to see where automation helps and where it gets in the way.