Senior Manager, Machine Learning Engineering
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- Build the platform foundation: lifecycle, data quality and engineering discipline
- Scale training and serving while closing the production learning loop
- Lead frontier ML systems, agentic delivery and the engineering organization
Benchmark: Metropolis Senior Manager, ML Engineering + OpenAI Engineering Manager, MLE + Adobe senior ML engineering leadership signals (Sep 2026) Market refresh: data and annotation pipelines; distributed training; model registry and lifecycle; low-latency serving; feature stores; observability and continual learning; GenAI/agentic deployment; architecture and reliability; roadmap ownership; vendor/data quality; coaching and managing ML/platform teams.
Who’s Hiring Right Now
US openings posted in the last 7 days at Metropolis, OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Build the platform foundation: lifecycle, data quality and engineering discipline
ML platform architecture, lifecycle + engineering standards
Coverage: ML Systems / MLOps / Platform Engineering / Technical Strategy
Data engineering, annotation + evaluation-ready datasets
Coverage: Data Pipelines / Feature Stores / Annotation / Versioning / Quality
Scale training and serving while closing the production learning loop
Distributed training + production inference infrastructure
Coverage: PyTorch / DDP / GPU Compute / Serving / Scalability
Deployment, observability + continual learning loops
Coverage: Deployment / Monitoring / Drift / Retraining / Rollouts / SLOs
Lead frontier ML systems, agentic delivery and the engineering organization
Performance engineering + production agentic AI
Coverage: Inference Optimization / LLM-VLM / Agents / Reliability / Cost
Agent architecture + senior ML engineering leadership
Coverage: Agent Design / Team Topology / Roadmaps / Coaching / Delivery
Role-Readiness Proof / Capstone
Lead the design of an enterprise ML engineering platform and operating model: define team topology and roadmap; design batch/stream data, annotation and evaluation pipelines; feature and dataset versioning; distributed training and experiment management; model registry and release gates; low-latency serving; observability, SLOs, rollback and retraining; GenAI/agent deployment with safety and human review; vendor/data-quality controls; and executive metrics for reliability, velocity, model quality and cost.
Proof Rubric
- Show architecture-level technical depth without becoming the bottleneck
- make build-vs-buy and cost/performance tradeoffs explicit
- connect data quality to model/evaluation quality
- demonstrate reproducible training and controlled releases
- define latency, throughput, availability and quality SLOs
- design monitoring and continual-learning triggers
- establish review and incident mechanisms
- coach senior engineers and managers
- and translate ML investment into measurable product and business outcomes