Senior Engineering Manager, ML Platform
Leads and grows the ML Platform team responsible for training, evaluating, and serving production machine-learning models. The role requires 8+ years of engineering experience, 4+ years managing engineering teams, strong ML systems expertise, and customer-facing technical leadership.
About the job
Responsibilities
- Lead and grow a team of ML platform engineers and data scientists; own roadmap, execution, and system quality.
- Review designs, unblock engineers on complex problems, and make architecture and trade-off decisions.
- Partner with strategic customers and Sales/Solutions Engineering on technical proof-of-value engagements.
- Reduce technical debt while balancing platform health with feature delivery.
- Mature offline and online model evaluation frameworks.
- Automate repeatable processes across training, evaluation, deployment, and monitoring.
- Align platform investments with Data Science, Core Infrastructure, Product, and Customer Success.
Requirements
- 8+ years of overall hands-on engineering experience, including 4+ years managing software or machine learning engineering teams.
- Deep technical fluency in model training pipelines, feature engineering, model serving, and production-scale evaluation.
- Experience leading technical customer engagements or proof-of-value projects with enterprise customers.
- Track record of reducing technical debt in high-traffic production systems.
- Experience designing or scaling machine-learning evaluation frameworks.
- Experience identifying repeatable manual processes and driving automation.
- Experience hiring, mentoring, and developing engineering talent.
- Bachelor's degree in Computer Science or a related technical discipline, or equivalent practical experience.
Nice-to-Haves
- Large-scale distributed ML infrastructure experience, including Spark, Flink, or Databricks.
- Familiarity with fraud detection, risk, or trust and safety.
- Hands-on GCP or AWS ML infrastructure experience.
- Streaming architecture experience, including Kafka.
- Containerized and orchestrated deployment experience with Docker and Kubernetes.
- Familiarity with AI coding assistants such as Claude Code.
Compensation and Benefits
- Annual salary: $240,000–$340,000.
- Competitive total compensation package.
- 401(k) plan.
- Medical, dental, and vision coverage.
- Wellness reimbursement.
- Education reimbursement.
- Flexible time off.
Skills
GCP, AWS, Spark, Apache Kafka, Kubernetes, Docker, Databricks, Python, Machine Learning, Feature Engineering, Model Serving, Model Evaluation, Distributed Systems, Streaming Architectures, Ai Coding Assistants
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