Senseye

Smartphone-based PTSD diagnostics via eye biomarkers

AI Engineer

Computer VisionPosted on Nov 12

Full-timeAustin, TX, USA$129k - $148kIn-person

About the Job

Requirements

A Master's degree or PhD in computer science, engineering, mathematics, or a related field, or equivalent experience demonstrating comparable machine-learning skills, such as several years of applied machine-learning work or substantial published or open-source work.
Experience with Python and at least one deep-learning framework.
A working grasp of machine-learning fundamentals, including training and validation, overfitting, evaluation metrics, and the distinction between test scores and product performance.
Hands-on experience with modern deep learning beyond off-the-shelf use, including training or substantially adapting models and explaining design choices and tradeoffs.
Comfort with Git, the command line, and working on a shared codebase.
Ability to explain results clearly in writing and verbally, including communicating uncertainty.
Curiosity and willingness to investigate anomalies in data or code.

Responsibilities

Train and evaluate modern deep-learning models on large datasets, including self-supervised approaches and fine-tuning large pretrained models.
Build and maintain data pipelines by collecting, cleaning, deduplicating, and versioning large video datasets.
Develop methods for learning from noisy or limited labels and measure the signal carried by models.
Run experiments carefully, track runs, make results reproducible, and document findings, including negative results.
Design evaluations that reflect real-world use and identify where offline metrics and product performance diverge.
Support existing computer-vision work as models move toward production.
Write clean, tested, well-documented Python that teammates can reuse.
Participate in code review and technical discussions and explain results to technical and non-technical colleagues.
Follow current AI research, reproduce promising methods, propose experiments, and teach the team what is learned.
Contribute to the team's broader needs as required.

Desired Qualifications

Hands-on experience with self-supervised, contrastive, or masked-modeling methods.
Experience with large-scale training, including multi-GPU runs or experiment tracking such as Weights & Biases.
Experience with video, time-series, or sensor data.
Experience with noisy-label learning or semi-supervised methods.
A paper, open-source contribution, or substantial personal project to demonstrate.
Exposure to healthcare, medical-device software, or other regulated settings.
Up to about three years of hands-on machine-learning experience, including graduate research, a thesis, internships, and industry work.

AI Engineer

Senseye · Austin, United States · Full-time · Posted 2026-11-12

Salary: USD 129,000–148,000

Workplace: on_site

Department: Engineering

Description

We're looking for an AI Engineer to help build the next generation of AI models behind our diagnostic platform. You'll work on modern, large-scale machine learning for health, turning large, messy, real-world datasets into models that carry real clinical signal.

You'll join a team of AI engineers who care about doing this the right way. We're looking for someone who brings current knowledge of the field, whether from graduate research or hands-on work, and who pushes the team to approach problems from a modern AI perspective. We want someone who understands why methods work, not someone who can only run a tutorial notebook on an off-the-shelf model. Together you'll assemble and curate large datasets, train and evaluate models, and show whether they carry real clinical signal.

Learning goes both ways here. You'll work alongside experienced AI engineers who review your code and talk through your results, and we expect you to teach them what's new. The work is hands-on from week one. Over time you'll own larger pieces of the pipeline, from data to deployed model.

Responsibilities

Train and evaluate modern deep learning models on large datasets, including self-supervised approaches and fine-tuning of large pretrained models.
Build and maintain the data pipelines that feed them: collecting, cleaning, deduplicating, and versioning large video datasets.
Develop ways to learn from noisy or limited labels, and measure how much real signal the models carry.
Run experiments carefully: track runs, make results reproducible, and write up what you found, including negative results.
Design evaluations that reflect real-world use, and show clearly where offline metrics and product performance diverge.
Support our existing computer vision work as models move toward production.
Write clean, tested, well-documented Python that teammates can pick up and reuse.
Take part in code review and technical discussions, and explain your results to both technical and non-technical colleagues.
Stay on top of current AI research. Reproduce promising methods, propose experiments, and teach the team what you learn.
Pitch in on whatever the team needs. We're small, and everyone does a bit of everything.

About Senseye

Senseye is a NeuroTechnology company based in Austin, TX, on the cusp of revolutionizing mental health. We're developing the world's first objective mental health diagnostics platform, measuring cognitive activity through the eye using mobile devices. Our initial diagnostic for PTSD is entering clinical trials, with additional indications for anxiety and depression to follow. We're a team of scientists, engineers, and clinicians building technology that could redefine how mental health is understood and treated.

Requirements

Master's degree or PhD in computer science, engineering, mathematics, or a related field, or equivalent experience showing comparable machine learning skills, such as several years of applied ML work or substantial published or open-source work.
Recent graduates are welcome. Up to about 3 years of hands-on ML experience, including graduate research, a thesis, internships, and industry work, is a plus.
Solid Python and experience with at least one deep learning framework.
A working grasp of ML fundamentals: training and validation, overfitting, evaluation metrics, and why a good test score isn't the same as a working product.
Hands-on experience with modern deep learning beyond off-the-shelf use. You've trained or substantially adapted models yourself and can explain your design choices and the tradeoffs.
Comfort with Git, the command line, and working on a shared codebase.
Clear writing and communication. You can explain a result or a plot simply and say what you're unsure of.
Curiosity and the habit of digging in when something looks off, in the data or in your own code.

Extra Points

None of these are required.

Hands-on experience with self-supervised, contrastive, or masked-modeling methods.
Experience with large-scale training: multi-GPU runs or experiment tracking such as Weights & Biases.
Experience with video, time-series, or sensor data.
Experience with noisy-label learning or semi-supervised methods.
A paper, open-source contribution, or substantial personal project you can show us.
Exposure to healthcare, medical device software, or other regulated settings.

Benefits

Flexible schedule and flexible paid time off
Health, dental, and vision insurance
Short- and long-term disability and life insurance
401(k)
Continuing education opportunities
Health and Wellness stipend
Fully stocked kitchen and a flexible hybrid environment
The freedom and trust to shape your role as we design, build, and ship our products

Apply

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About the Company

Senseye

Smartphone-based PTSD diagnostics via eye biomarkers

Senseye creates a software-based diagnostic tool for mental health that starts with PTSD. It uses a smartphone app to record eye responses with the front camera while users complete simple visual tasks, then analyzes pupil gaze and iris biomechanics with a proprietary machine learning model to generate a diagnostic report and a severity score within minutes. Unlike traditional assessments, it provides an objective measure on a standard phone, enabling remote or in-clinic use without specialized hardware and reducing reliance on patient self-reporting. The long-term goal is to expand into a panel diagnostic tool for other conditions like MDD and GAD to help clinicians, pharmaceutical companies, and payers establish baselines and track treatment outcomes over time.

Company size: 11-50
Company stage: Series A
Total funding: $8M
Headquarters: Austin, TX
Founded: 2015

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