Director of Machine Learning/ Hybrid/ Austin, TX

We are building the world’s first objective mental health diagnostics platform, using mobile-based neurotechnology to measure cognitive activity through the eye. After six years of R&D and multiple clinical iterations, we are now entering trials for our PTSD diagnostic, with additional indications for Anxiety and Depression to follow. As the world faces a growing mental health crisis, we are creating a scalable, objective, and clinically validated approach to diagnosing mental health conditions. This role exists to help build the ML systems that will power this platform and accelerate our impact on global mental health.

We are seeking a Director of Machine Learning who is both a strategic leader and a hands-on technical contributor. You will build and guide a small, high-performing ML team while also driving model development, experimentation, deployment, and evaluation. You will establish the processes, standards, and infrastructure required to translate research concepts into robust, production-grade diagnostic systems. This role is ideal for someone who thrives in ambiguous, high-stakes environments and wants to directly influence the future of applied ML in digital medicine.

What you will do

• Own and drive the ML technical roadmap across research, development, and production.

• Design, build, and deploy models for computer vision and time-series applications, including segmentation, keypoint detection, gaze tracking, and biosignal extraction.

• Lead, mentor, and grow a team of ML engineers and scientists while establishing strong technical rigor and experimentation discipline.

• Implement reproducible workflows including dataset versioning, experiment tracking, GPU scheduling, and documentation.

• Develop objective evaluation frameworks to assess model performance in development, clinical testing, and production.

• Build clear reporting processes that communicate model behavior, data quality, and performance metrics to technical and executive stakeholders.

• Collaborate with product, clinical, and platform engineering teams to align ML capabilities with regulatory, user, and product requirements.

• Plan and manage ML compute resources, infrastructure scaling, and data pipeline optimization.

• Translate ambiguous scientific and product problems into clear ML problem statements and solution paths.

• Integrate emerging research and tools when they meaningfully improve clinical accuracy, scalability, or efficiency.

• Represent ML leadership in external collaborations, presentations, and publications.

What you bring

• 7+ years of applied ML experience, including 2+ years in a leadership or staff-level role.

• Proven success deploying ML models to production and maintaining long-term performance.

• Expertise in computer vision and time-series modeling, ideally within video, camera-based, or biosignal contexts.

• Strong experience extracting signal from noisy data and applying statistical modeling.

• Proficiency in Python and at least one major deep learning framework such as PyTorch, JAX, or TensorFlow.

• Experience with ML systems in healthcare or regulated environments, including validation and auditability.

• Ability to define clear metrics and communicate results to both technical and non-technical audiences.

• Familiarity with MLOps tooling such as Weights and Biases or MLflow.

• Strong planning skills, including managing GPU resources and prioritizing team workloads.

• Excellent written and verbal communication focused on clarity and collaboration.