Machine Learning Engineer

We're seeking an experienced ML Engineer with a strong background in computer vision and time-series modeling. You're skilled at every stage of the ML lifecycle—from defining problems and exploring data, to developing, deploying, and iterating on models. You adeptly balance rigorous modeling with practical software engineering, integrating ML solutions seamlessly into production systems. You understand the nuances between offline metrics and real-world performance, translating complex, abstract problems into actionable ML strategies.

You thrive in ambiguous, fast-paced environments, approaching challenges with curiosity and analytical rigor. Complex, unsolved problems, particularly those involving visual data and behavioral analysis, motivate you. You're passionate about making tangible impacts by taking ownership of projects from initial prototype through production deployment.

Senseye is a Neuro Technology Company in Austin, TX on the cusp of revolutionizing Mental Health. Over the past 6 years we have invested millions of dollars in R&D to build our platform allowing us to measure cognitive activity via the eye through mobile phones. Through multiple iterations and use cases we are now focused on building the world's first objective mental health diagnostic on top of our core technology.

Our first diagnostic is for PTSD and is entering clinical trials now, followed soon by additional indications for Anxiety and Depression. As the world struggles with a mental health crisis, it is not hyperbolic to suggest that an objective diagnostic platform, that gives clinicians a safe and objective accurate approach to identifying and monitoring mental health disease, will redefine how mental health services are provided, and will enable access to treatment for millions of sufferers.

The Senseye platform has the potential to be the technology that drives this change. This is a great opportunity to shape the future of digital medicine and address unmet medical needs that affect billions of people worldwide.

• Design, develop, and deploy ML models focused on computer vision and time-series analysis (e.g., semantic segmentation, point-of-gaze tracking, keypoint detection, photoplethysmography, MAMBA, dilated 1D CNNs, sparse attention transformers).
• Select optimal architectures and training methods that align with constraints around data collection, annotation quality, timelines, and budgets, such as incorporating semi-supervised and few-shot techniques when applicable.
• Develop and maintain robust, production-grade ML services supporting critical user workflows.
• Monitor production model performance, establishing evaluation frameworks that accurately reflect real-world use.
• Conduct comprehensive exploratory data analyses to guide problem scoping, feature engineering, and model selection.
• Collaborate closely with platform and infrastructure teams to optimize ML tooling and data pipelines.
• Translate ambiguous business needs into structured ML problems and actionable technical roadmaps.
• Identify new data signals, design experiments, and iteratively refine models to maximize user impact.
• Review, contribute, and maintain high-quality, testable, and well-documented ML pipelines.
• Lead technical discussions and clearly communicate modeling strategies and outcomes to internal stakeholders.
• Stay informed on emerging research and tools, integrating them effectively when beneficial.

• 5+ years of applied ML experience, including deploying models to production.
• Expertise in computer vision and/or time-series modeling, with experience in video or camera-based systems strongly preferred.
• Strong statistical modeling skills, particularly in extracting signals from noisy datasets.
• Proficiency in Python and deep learning frameworks (PyTorch, JAX, Tensor Flow).
• Proven track record of translating academic research into scalable, practical ML solutions.
• Experience with large-scale data systems, MLOps tooling, and model versioning practices.
• Abilit Comfort managing ambiguity and independently driving projects to completion to clearly communicate technical solutions and their business impact to varied audiences.

• Expert-level proficiency in Python and at least one deep learning framework (PyTorch, JAX, etc.).
• Experience adapting recent research into innovative methods tailored to business needs.
• Demonstrated creative problem-solving ability, evidenced by patents or novel techniques.
• Experience managing scientific projects within industry or academia.
• Excellent written and verbal communication skills, able to bridge technical and non-technical audiences effectively.

• Direct experience with computer vision or camera/video-based systems.
• Ownership, deployment, and maintenance of model-serving pipelines in production.
• Expert-level coding skills focused on clarity, efficiency, and maintainability, complemented by rigorous testing practices.
• Experience with probabilistic modeling or Bayesian approaches.
• Familiarity with…