ML Platform Engineer

Job description

Job responsibilities

• Build the production platform that enables Hadrian’s factories to safely depend on models for drawing extraction, cycle-time prediction, forecasting, scheduling, and more—with measurable performance and fast rollback.
• Develop shared batch and online serving for tabular, vision, document-AI, scheduling, graph, and embedding workloads, targeting clear SLAs for latency, availability, and isolation.
• Create repeatable release and evaluation processes featuring automated tests, reproducible artifacts, lineage, shadow deployments, canaries, and A/B tests.
• Own online feature serving and maintain contract integrity with offline feature tables; proactively detect and address training-serving skew, feature drift, bad data, and model degradation.
• Build operational tooling for telemetry, incident response, autoscaling, resource and GPU management, cost attribution, and secure model routing.
• Develop APIs, SDKs, reusable templates, and documentation that teams can adopt without requiring close support from platform engineers.

Job requirements

• Track record building and operating production ML infrastructure across multiple models or inference workloads.
• Strong production-level Python and SQL skills, including typing, testing, packaging, API design, and building observability features.
• Hands-on experience with Kubernetes, containers, and handling distributed-system failure modes such as retries, partial failures, idempotence, and resource isolation.
• Engineering background with model registries, feature systems, batch/real-time inference, experiment tracking, or model CI/CD workflows.
• Practical judgment around latency, throughput, availability, multi-tenancy, autoscaling, and infrastructure cost optimizations.
• Ability to build stable interfaces and collaborate closely with engineering and scientific stakeholders.