Director, AI & Machine Learning

. Set AI and ML strategy and roadmap across R&D, clinical, medical, operations, and commercial priorities .

Posted 9/25/2026full-timeRemote • United StatesLead💰 $242,543 - $328,147 per yearWebsite

Tools & technologies

CloudPythonPyTorchTensorflow

Key responsibilities & impact

Set AI and ML strategy and roadmap across R&D, clinical, medical, operations, and commercial priorities
Lead advanced ML, generative AI, foundation-model, and agentic AI programs from opportunity framing through production adoption
Establish production ML architecture, platform choices, modernization roadmaps, reusable engineering patterns, and quality standards
Guide MLOps and data platform strategy, including data quality, lineage, experimentation, evaluation, monitoring, model registry, release governance, and lifecycle operations
Champion responsible AI and model governance across the portfolio
Recruit, develop, and retain ML engineering talent and build a culture of technical excellence
Define investment logic, success measures, and portfolio-level KPIs
Measure adoption and value of scientific AI capabilities
Partner with scientists, technology leaders, data/platform teams, quality, legal, compliance, privacy, and information security
Develop clear technical narratives, roadmaps, recommendations, and investment decisions for senior stakeholders

What you’ll need

Doctorate degree and 4 years of Director, AI & Machine Learning experience, OR Master’s degree and 8 years of such experience, OR Bachelor’s degree and 10 years of such experience
At least 4 years of direct people management and/or leadership experience leading teams, projects, programs, or resource allocation
Expert AI/ML engineering knowledge and technical strategy experience for scientific research applications
Deep hands-on experience with software engineering and production AI/ML system design
Experience with scalable APIs, pipelines, cloud platforms, model serving, evaluation, observability, and maintainable architecture
Experience directing data and MLOps capabilities, including lineage, reproducibility, validation, monitoring, drift detection, CI/CD, incident response, auditability, and model retirement
Ability to implement responsible AI governance, validation evidence, model documentation, risk controls, access safeguards, and review mechanisms
Demonstrated success building and leading high-performing technical teams
Strong stakeholder management and cross-functional alliance-building experience
Experience setting strategy for foundation models and enterprise AI adoption
Demonstrated experience with Python and modern ML/deep-learning frameworks such as PyTorch, TensorFlow, or JAX
Experience with cloud and data platforms used to deploy AI/ML solutions at scale

Comp & perks