Director of Machine Learning

Senseye•Austin, TX

35d•$200,000 - $300,000

About The Position

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. This role is both strategic and hands-on: you’ll define the vision and processes for our ML organization while also contributing directly to model development, experimentation, and deployment. You’ll lead a small but growing team of ML engineers and scientists working at the intersection of computer vision, time-series analysis, and cognitive neuroscience. You’ll establish best practices for reproducibility, evaluation, and experimentation — transforming research concepts into robust, clinically validated production systems. You’ll work closely with leadership to define roadmaps, resource needs, and decision frameworks that are grounded in data and operational transparency. This role is ideal for a technical leader who thrives in ambiguous, high-stakes environments, enjoys building teams and systems from the ground up, and wants to directly influence how ML is used to transform mental health diagnostics.

Requirements

7+ years of applied ML experience, including at least 2+ years in a leadership or staff-level role.
Proven success deploying ML models to production and maintaining their performance over time.
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.
Proficiency in Python and at least one major deep learning framework (PyTorch, JAX, TensorFlow).
Experience building ML systems in healthcare or regulated environments, with understanding of validation, auditability, and compliance processes.
Demonstrated ability to define and communicate objective metrics, build dashboards or reports, and translate results for technical and non-technical audiences.
Familiarity with MLOps tooling (e.g., Weights & Biases, MLflow), experiment tracking, and infrastructure management.
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.

Nice To Haves

Experience leading ML teams in production settings, including roadmap development and hiring.
Experience integrating ML within SaMD or clinical research pipelines.
Track record of adapting cutting-edge research into production-ready methods.
Contributions to open-source ML frameworks or published research.
Experience with probabilistic modeling or Bayesian inference.
Prior experience implementing metrics-driven decision frameworks across teams.

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).
Lead and mentor a small, high-performing team of ML engineers and scientists; establish technical rigor, code quality, and experimentation culture.
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.
Collaborate cross-functionally with product, clinical, and platform teams to align ML outputs with product goals, user impact, and regulatory requirements.
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.
Stay ahead of emerging research and tools, integrating new approaches thoughtfully when they advance clinical accuracy, scalability, or efficiency.
Represent ML leadership externally, ensuring Senseye’s technical excellence and ethical standards are visible in publications, presentations, and collaborations.

Benefits

The freedom and trust to define your role as we design, build, and ship our products
Competitive salary and stock option plan
Flexible paid time off (vacation, sick leave, and public holidays)
Flexible schedules
Company health care plan
Medical, dental, and vision insurance
Short and long term disability insurance
Life insurance policy
401k
Commuter benefits for parking, public transit, carshares, etc.
Mothers' room
Fully stocked kitchen
Opportunities for continuing education