Platform Engineering Team Lead - Remote

5+ years experience building distributed systems; 3+ years in MLOps/ML platform engineering (or equivalent impact). Knowledge of Linux/OS internals (process model, cgroups/namespaces), networking (TCP/IP, HTTP/2), concurrency, and performance profiling. Deep understanding of Kubernetes (bonus: Mesos). Proficiency developing high-performance services in Java, Rust, Go or C++ (bonus: familiarity with vert.x and Netty frameworks); strong Python skills. Experience with GPU infrastructure (scheduling, containerization, optimization). Track record of designing and operating model platforms (registry, training, serving, monitoring) in production. Demonstrated success leading technical teams and implementing organization-wide platform solutions.

Design and evolve the core platform (Kubernetes, Mesos, GPU scheduling/autoscaling, distributed compute). Own the model platform: registry, experiment tracking, training orchestration, evaluation, serving, and monitoring. Build the Golden Path: reference repos, a scaffold CLI, opinionated CI/CD pipelines, runtime contracts, high-performance clients, circuit breakers and other production‑ready defaults. Operate a secure, multi‑tenant model registry and training platform with standardized experiment/evaluation harnesses. Provide turnkey serving patterns (online + batch), drift/quality monitoring, and rollback playbooks. Integrate public/open‑source AI capabilities as managed platform services with cost and data‑governance guardrails. Run the squad: roadmap/prioritization, delivery, mentoring, and high engineering standards. Partner with product engineering (Zyte API, Scrapy Cloud), Prod Ops, and Security on adoption and rollout plans. Mentor the team and foster a platform-thinking mindset.