Core & ML Ops Team Lead - Remote job opportunity at Zyte.

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Date2025-11-06T12:36:07.399Z bot
Zyte Core & ML Ops Team Lead - Remote
Experience: 5-years
Pattern: full-time
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degreeHigh School (S.S.C.E)
loacation Barcelona, Spain
loacation Barcelona....Spain
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About Us At Zyte, we eat data for breakfast and you can eat your breakfast anywhere and work for Zyte. Founded in 2010, we are a globally distributed team of over 250 Zytans working from over 28 countries who are on a mission to enable our customers to extract the data they need to continue to innovate and grow their businesses. We believe that all businesses deserve a smooth pathway to data. For more than a decade, Zyte has led the way in building powerful, easy-to-use tools to collect, format, and deliver web data, quickly, dependably, and at scale. And today, the data we extract helps thousands of organizations make smarter business decisions, secure competitive advantage, and drive sustainable growth. Today, over 3,000 companies and 1 million developers rely on our tools and services to get the data they need from the web. Zyte is seeking an experienced Team Lead to manage our Core & MLOps Squad, responsible for "Building the bedrock infrastructure that powers Zyte at scale." This hands-on technical leadership role requires expertise across MLOps, systems programming, and orchestration to lead a cross-functional team in designing and maintaining the scalable foundation that enables all Zyte teams to build and run their services with confidence. What you’ll do Technical Leadership 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 (health/metrics/tracing/SLOs), high-performance clients, circuit breakers and other production‑ready defaults. MLOps Excellence 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. Team Management 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. Ownership Areas Container orchestration (Kubernetes/Knative), GPU provisioning & autoscaling, environment & secret management. Operators, sidecars, and internal SDKs/libraries (Go/Rust/Python/Java) that enforce the golden path contract. Model platform: registry, experiment tracking, training orchestration, evaluation framework, serving infra, model monitoring. Observability: logging/metrics/tracing pipelines; Billing pipeline: metering/events/cost tracking abstractions. Golden Path : Java, Python, ML templates + CI/CD blueprints + docs + scaffold CLI. Reliability enablement (SRE practices), cost governance, supply‑chain security (SBOM, image signing). Qualifications Required 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. Preferred Streaming & workflows: Kafka plus Argo/Temporal/Airflow or equivalents. eBPF‑based observability, perf tooling, or io_uring experience Cost optimization for ML/AI; multi‑tenant quotas and fairness. Hands‑on experience authoring Golden Paths (service chassis/templates, CI/CD blueprints, CLI scaffolds). SRE practices (SLIs/SLOs, incident management)

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