Manager, ML Engineering
United States · Remote · Permanent
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What the market looks like
We've tracked 300+ management-level postings in AI operations over the last six months, concentrated in technology, financial services, and professional services hubs across California, New York, and Texas. Compensation for this cohort ranges from $150K to $280K annually. The strongest candidates bring hands-on experience shipping ML systems in production, combined with the ability to scale teams and processes—they own both the technical depth to lead engineers and the systems thinking to embed ML into organizational workflows.
Job responsibilities
Lead and grow an ML engineering team, hiring for technical depth and problem-solving mindset while fostering a culture of production-first thinking
Own the end-to-end ML pipeline—data ingestion, model training, validation, and deployment—ensuring systems meet latency and accuracy requirements in production
Partner with data science and data engineering teams to translate model research into scalable, maintainable systems
Drive infrastructure and tooling decisions that reduce friction in model development, testing, and deployment cycles
Work with product and business stakeholders to define success metrics and ensure ML initiatives align with organizational priorities
Build and maintain documentation, best practices, and playbooks for model monitoring, retraining, and incident response
Identify and address technical debt and operational bottlenecks in ML systems before they impact reliability
Candidate requirements
5+ years of experience in ML engineering, MLOps, or data engineering roles, with at least 2 years in a leadership or senior IC capacity managing ML systems at scale
Demonstrated track record shipping and operating ML models in production, including experience with model deployment, monitoring, and troubleshooting in live environments
Solid grasp of ML fundamentals (feature engineering, model evaluation, overfitting, data drift) and comfort communicating trade-offs between model performance and operational complexity
Experience building or scaling engineering teams and mentoring others—showing ability to hire, unblock engineers, and raise technical bar
Fluency with modern ML tools and platforms (Python, Git, cloud platforms like AWS/GCP/Azure, containerization, orchestration frameworks)