Director, Machine Learning
Austin, TX · Hybrid · Permanent
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What the market looks like
We've tracked 200+ senior-level ML engineering postings across the US in the last six months, with concentrated hiring in California, Washington, and New York. Directors in this function typically land in the $240K–$400K range and lead both technical strategy and team delivery. The strongest candidates bring 8+ years of hands-on ML work — from model development through production systems — paired with experience scaling teams, shipping infrastructure, and translating business problems into ML roadmaps. Hiring accelerates in technology, financial services, manufacturing, and healthcare sectors, where organizations are embedding ML into core products and operations.
Job responsibilities
Own the end-to-end ML engineering strategy — model development, feature engineering, experimentation infrastructure, and production deployment — aligned to business outcomes
Lead and scale an ML engineering team (typically 5–15 engineers), including hiring, mentorship, technical direction, and performance management
Partner with product, data science, and platform engineering to define ML roadmaps, prioritize initiatives, and ship models and systems on timeline
Build and maintain ML infrastructure, pipelines, and monitoring systems that enable reproducibility, observability, and continuous improvement
Drive technical rigor across experimentation design, model evaluation, and performance measurement; establish and enforce standards for code quality and documentation
Communicate progress, trade-offs, and technical decisions to executive and non-technical stakeholders; translate business requirements into technical scope
Contribute to hiring, retention, and technical culture — recruiting talent, unblocking team bottlenecks, and fostering a learning environment
Candidate requirements
8+ years in machine learning, with substantial hands-on experience building, training, and deploying models in production systems
3+ years leading or managing ML or engineering teams; demonstrated success scaling people, setting technical direction, and shipping initiatives
Deep familiarity with ML infrastructure, MLOps, data pipelines, and monitoring — not just modeling; understand the gap between research and production
Fluency with common ML frameworks and tools (PyTorch, TensorFlow, scikit-learn, SQL, cloud ML platforms); comfort working across full stack (data → model → inference)
Proven ability to partner cross-functionally with product, data, and engineering teams; translate business problems into technical roadmaps and ship results
Track record of building high-performing, inclusive teams; experience with hiring, mentorship, and retaining strong engineers