Director, ML Engineering
New York, NY · Hybrid · Permanent
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What the market looks like
We've tracked 200+ senior-level ML Engineering postings in the US over the last six months, with the strongest concentration in New York, San Francisco, and Seattle. These roles span technology, financial services, manufacturing, healthcare, and professional services—sectors moving fast on production ML systems. Compensation for directors in this cohort ranges from $240K to $400K base salary. The strongest candidates bring hands-on experience shipping models to production, track records scaling teams from individual contributors to 5–15 person groups, and the ability to balance technical depth with people leadership and cross-functional stakeholder management.
Job responsibilities
Own the ML engineering roadmap and technical strategy, setting priorities that align model development with business outcomes and infrastructure capability
Build and scale an ML engineering team—hire, mentor, and develop individual contributors and senior engineers; establish hiring bar and culture for technical rigor
Drive end-to-end model delivery, from experimentation and training through deployment, monitoring, and retraining in production environments
Partner with data science, platform, and product teams to define ML systems architecture, data pipelines, and feature infrastructure that support rapid iteration
Lead technical due diligence on ML tools, frameworks, and infrastructure; make build-versus-buy decisions and manage vendor relationships
Establish observability and governance practices for model performance, data quality, and system reliability across production systems
Communicate technical progress, risks, and resource needs to executive leadership and cross-functional stakeholders in business terms
Candidate requirements
7+ years of experience in machine learning engineering, with at least 3+ years in a leadership or senior IC role managing production ML systems
Demonstrated ability to ship models to production and own their performance, stability, and cost in live environments
Experience building or scaling an ML engineering team—hiring, mentoring, and setting technical direction for a group of 4+ engineers
Strong technical foundation in Python, ML frameworks (TensorFlow, PyTorch, scikit-learn), and data pipeline tools; hands-on ability to code alongside the team
Track record communicating technical strategy and trade-offs to non-technical stakeholders and translating business priorities into engineering plans
Comfort operating in hybrid or remote settings and collaborating across distributed or co-located teams