Senior ML Engineer​/Hybrid​/Austin, TX​/Startup

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 iterations, we are entering clinical trials for our PTSD diagnostic with 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 design and deliver the ML systems that will power this platform and enable clinicians to access accurate, real-time diagnostic signals.

We are seeking an experienced ML Engineer with strong expertise in computer vision and time-series modeling. You will work across the entire ML lifecycle, from data exploration to modeling to production deployment. You excel at breaking down ambiguous problems, running rigorous experiments, and building production-grade systems. This is a hands‑on role suited for someone who thrives in fast‑paced environments, enjoys solving open-ended technical challenges, and wants to make a direct impact on the future of digital mental health.

• Design, develop, and deploy ML models for computer vision and time-series applications such as segmentation, gaze tracking, keypoint detection, biosignal extraction, and transformer‑based architectures.
• Select architectures and training approaches that account for real‑world constraints in data quality, timelines, and budgets, including semi‑supervised and few‑shot methods.
• Build and maintain reliable, scalable ML services that support core product workflows.
• Monitor live model performance and establish evaluation frameworks that reflect real‑world user behavior.
• Conduct exploratory data analysis to inform scoping, feature design, and model strategy.
• Work closely with platform and infrastructure teams to optimize ML tooling, pipelines, and data flow.
• Translate complex or ambiguous business needs into structured ML problems and clear solution paths.
• Identify new data signals, run experiments, and iterate on models to maximize clinical and user impact.
• Write and maintain high‑quality, testable, well‑documented ML pipelines and codebases.
• Lead technical discussions and communicate modeling decisions and results to stakeholders across the company.
• Integrate emerging research and tools when they meaningfully improve performance or efficiency.

• 5 years of applied ML experience, including production deployment of models.
• Expertise in computer vision and time-series modeling, ideally in camera-based or video-based systems.
• Strong experience extracting signal from noisy datasets and applying statistical modeling techniques.
• Proficiency in Python and deep learning frameworks such as PyTorch, JAX, or Tensor Flow.
• Proven ability to translate research concepts into scalable production ML systems.
• Experience with large-scale data systems, MLOps tooling, and model versioning.
• Ability to manage ambiguity and independently drive projects to completion.
• Strong communication skills with the ability to explain technical decisions and business impact.