Machine Learning Manager

The Role

As the ML Manager, you will lead the Machine Learning team of researchers and engineers to build cutting-edge voice analysis models from early experimentation through production deployment. You will communicate directly with executive leadership on recent findings, strategy and operational requirements for the success of your team. You will work side-by-side with the software engineering and product leadership to ensure the success of ML models in production. You will collaborate with our clinical team to design data collection strategies and expand our clinically validated datasets.

Responsibilities

• Lead ML team in the technical aspects of core-product development including investigating, planning, executing, evaluating, and reporting on the entire MLOps pipeline from problem definition, data requirements, experimentation, productization, reporting, and monitoring
• Be self-directed to work independently and in a multidisciplinary team environment
• Roll up your sleeves and work alongside team members on portions of projects to ensure completion of high priority tasks and critical non-project work
• Advise, direct, coach, inspire and review the work of early-career team members on complex projects
• Manage the workload, interteam communication, conflict resolution and career development of the ML staff
• Lead the planning, execution and retros for the ML team sprints
• Develop documentation around ML team processes, maintain the ML wiki and monitor compliance across team members
• Build, maintain and report on the technical roadmap and quarterly OKRs for ML workstreams, milestones and deliverables in collaboration with the product and engineering teams
• Develop and manage the ML project plans, identifying critical paths and resources required including ML budgets
• Evaluate and make recommendations concerning the application of new datasets, processing technologies, and stays apprised of technologies used by other voice processing companies by reviewing technical literature, attending seminars, and building thought leadership in voice biomarkers. Evaluate the applicability of those technologies within the organization.

Qualifications

• M.S./Ph.D. in Computer Science with 5+ years of experience building production-grade machine learning models in industry settings
• Minimum of 8 years of experience in product development
• 3+ years of experience managing R&D or ML projects
• Strong programming skills in python with extensive experience with the scientific and deep-learning stack (numpy, pandas, numba, torch, tensorflow, jupyter)
• A proven track record of building end-to-end deep-learning models
• Experience with ML model efficiency optimization for production deployment
• Ambitious team player with strong communication skills (oral and written)
• Experience implementing and experimenting with cutting-edge ML techniques from the latest literature
• Experience with cloud services (GCP, AWS, Azure) for training, data processing and/or model deployment
• Interpersonal skills required to communicate, advise, negotiate or influence others, as well as, build productive working relationships in various settings

Bonus Qualifications

• Background in speech processing/recognition or audio classification
• Experience with experiment tracking and reproducibility tools (MLFlow, WandB, DataBricks, etc)
• Experience with sprint management and Agile methodologies
• Recent publication(s) in peer-reviewed AI journals
• Experience with FDA approvals for software-as-a-medical device (SaMD)
  

Key Competencies

• Empathetic leader and mentor that is driven by the success of their team
• Comprehensive multidisciplinary skills for everything from code review to ML research to product documentation, with the ability to adapt to different challenges and collaborate effectively with cross-functional teams
• Strategic and agile planner with an eye for MVP
• Efficient communication skills with a reliable pattern of providing quick feedback and acknowledgments, along with the ability to complete larger projects within established timelines