About the job
Datadog’s CodeGen team builds systems that use AI to read, understand, generate, and safely change real code — powering automated fixes, CI/PR automation, agentic remediation, and Infrastructure-as-Code workflows. The team ties code understanding to observability so assistants can explain problems and produce safe, auditable code changes. We’re a new team building AI-assisted tools to make Datadog developers more effective, by autonomously generating tests, fixing bugs, and improving performance. We’re looking for a product-minded generalist to help us quickly define and ship products that make all Datadog customers 10x developers.
Responsibilities
Lead a team of engineers responsible for building production systems that enable code understanding and automated code changes — from parsers and telemetry ingestion to model serving, evaluation, and PR automation.
Drive technical direction and execution: set a clear roadmap, prioritize work, remove blockers, and raise quality and reliability bar for codegen features (safety, correctness, performance).
Partner with product, applied research, infra/SRE, security, and other engineering teams to ship end-to-end experiences that safely apply model outputs (PRs, CI checks, automated apply flows).
Build and maintain robust evaluation and monitoring pipelines (offline and online) to measure model quality, drift, and downstream correctness of code changes.
Own hiring, performance development, 1:1s, and career growth for your reports; grow a high-performing, inclusive team.
Be accountable for production readiness: on-call expectations, postmortems, SLIs/SLOs, and operational playbooks for codegen services.
Maintain engineering rigor around data pipelines, model inputs, determinism, reproducibility, and reproducible CI/CD for model + infra changes.
Qualifications
Minimum
Proven experience in software engineering and applied science, with a focus on engineering LLM-based systems in production
Demonstrated experience managing small teams of software engineers and/or applied scientists, with a track record of delivering high-quality products
Strong software development skills and proficiency in Python and Go
Strong understanding of machine learning theory, statistics, and fundamentals
Excellent communication abilities to convey complex technical concepts clearly
A collaborative mindset and proven experience in working in cross-functional teams
A proactive approach with a passion for continuous learning and innovation
Demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output
Preferred
No preferred qualifications listed.