Staff Full Stack Engineer, Lilt

• 5+ years of professional full-stack software engineering experience, with a track record of owning production systems end-to-end across more than one runtime/language
• Deep experience with a Python backend framework (FastAPI or comparable) plus async SQLAlchemy/Postgres, alongside production experience in at least one statically-typed backend language (Go, Java, or similar)
• Strong React/TypeScript frontend experience — component architecture, state management (Zustand, Redux, or similar), and a modern data-fetching layer (TanStack Query or comparable)
• Experience building and operating background job/worker systems (queue-driven or polling-based) with failure tolerance and idempotency in mind
• Experience integrating with third-party and platform APIs — including the GitHub API, OAuth/OIDC SSO, and at least one LLM API (Gemini, OpenAI, or similar) — handling auth, rate limits, and webhook-driven sync
• Experience building Slack (or comparable chat-platform) bot integrations that automate internal workflows — resource provisioning, approvals, budget/TTL enforcement — with real operational guardrails, not just CRUD features
• Comfort reading and extending applied-statistics or ML-adjacent code (agreement metrics, audio-quality scoring, or comparable data-quality tooling)
• Solid grasp of CI/CD, containerized deployment (Docker, Helm, ArgoCD/GitOps or comparable), and infrastructure-as-code (Terraform or comparable)
• Experience debugging and optimizing native-library (numpy/scipy/onnxruntime-class) memory growth in long-running Python worker processes via safe, boundary-aware process recycling — not just raising memory limits
• Experience building and owning internal platforms/tools that increase leverage for a non-engineering team (research, operations, support, data/workforce management, or similar) — not solely external-customer-facing product work

Desirable

• This platform sits directly on the critical path of paid customer deliverables with real SLAs, maintained by a small, high-leverage engineering team that produces reliable, production-ready tooling
• You will own significant surface area across both end-to-end: architecture, data-pipeline design, and the hands-on engineering that keeps this infrastructure reliable
• This is a high-impact, high-visibility role: the reliability and craft you bring directly enables on-time, high-quality delivery for some of the most prominent AI labs in the world
• In this role, you will drive the long-term technical strategy for internal platforms while working directly with the delivery, ops, and engineering teams across the business who depend on them daily
• This team ships cross-service automation in careful stages — advisory first, then assistive, only later decision-relevant, always with a human fallback — and you'll be expected to hold that same bar as you extend these systems
• You will partner with a small existing team to raise the engineering bar across two very different runtimes, set technical direction, and make the calls that determine how this infrastructure scales as the business grows
• Partner with the benchmarking business's researchers and TPMs to translate new benchmark and data-quality requirements into scoped technical designs
• Own the internal workforce-management tools on our internal platform for AI delivery for hundreds of external contributors: vetting flows, candidate assessment, QC, payment/delivery tracking, and roster/reporting exports
• Own architecture and long-term technical direction across multiple services and the platform
• Extend IAA and audio-QA pipelines: annotator outlier detection, ASR sidecar enhancements, LLM-based QC, and DNSMOS/librosa audio-quality scoring
• Design and ship new modules on the platform that plug new benchmark and vetting workflows into the existing multi-stage review lifecycle
• Own and extend the platform's API key provisioning and budget-governance system
• Build self-serve ChatOps-style automation for the internal engineering org and contributor base to enable accelerated annotator workflows and query resolution
• Harden background worker and job-processing infrastructure
• Set and enforce testing, CI/CD, and deployment practices across both codebases — from unit/integration testing through infrastructure-as-code and release automation
• Raise the technical bar for a small, high-leverage team through code review, design docs, and mentoring as the surface area grows