Senseye is seeking a Director of Machine Learning to lead the design, deployment, and evolution of our ML systems powering the world’s first objective mental health diagnostics platform.
Requirements
- Proficiency in Python and at least one major deep learning framework (PyTorch, JAX, TensorFlow).
- Expertise in computer vision and/or time-series modeling, ideally within video, camera-based, or biosignal contexts.
- Strong foundation in statistical modeling and signal extraction from noisy data.
- Familiarity with MLOps tooling (e.g., Weights & Biases, MLflow), experiment tracking, and infrastructure management.
- Proven success deploying ML models to production and maintaining their performance over time.
- Experience building ML systems in healthcare or regulated environments, with understanding of validation, auditability, and compliance processes.
- Track record of adapting cutting-edge research into production-ready methods.
Responsibilities
- Own and drive the ML technical roadmap, balancing short-term delivery with long-term research and infrastructure investments.
- Design, develop, and deploy models for computer vision and time-series applications (e.g., semantic segmentation, point-of-gaze tracking, keypoint detection, photoplethysmography, MAMBA, dilated 1D CNNs, sparse attention transformers).
- Implement reproducible ML processes — from dataset versioning and model tracking (e.g., Weights & Biases) to GPU scheduling, experiment orchestration, and results documentation.
- Develop and enforce objective evaluation frameworks to assess model performance and reliability across development and production environments.
- Build transparency and accountability through clear reporting of model metrics, data quality, and production outcomes to both technical and executive audiences.
- Guide operational planning for ML compute resources, infrastructure scaling, and data pipeline optimization.
- Translate ambiguous problems into clear ML problem statements, balancing technical feasibility, scientific value, and business impact.
Other
- 7+ years of applied ML experience, including at least 2+ years in a leadership or staff-level role.
- Demonstrated ability to define and communicate objective metrics, build dashboards or reports, and translate results for technical and non-technical audiences.
- Strong organizational and planning skills — able to manage GPU resources, schedule experiments efficiently, and prioritize workloads across the team.
- Exceptional written and verbal communication skills, with a focus on clarity, transparency, and collaboration.
- Experience leading ML teams in production settings, including roadmap development and hiring.
