AI Engineer

About the Role

We are seeking an AI Engineer to contribute to building the next generation of AI models for our diagnostic platform. This role involves working on modern, large-scale machine learning for health, transforming real-world datasets into clinically significant models. You will join a team of AI engineers focused on executing these tasks effectively. We are looking for individuals with current knowledge in the field, whether from graduate research or hands-on work, who can bring a modern AI perspective to problem-solving. The ability to understand the underlying principles of methods, rather than just executing tutorial notebooks, is valued.

Key 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 data pipelines for collecting, cleaning, deduplicating, and versioning large video datasets.
• Develop methods for learning from noisy or limited labels and measure the clinical signal carried by models.
• Conduct careful experiments, track runs, ensure reproducibility, and document findings, including negative results.
• Design evaluations that reflect real-world use and clearly delineate differences between offline metrics and product performance.
• Support existing computer vision work as models progress towards production.
• Write clean, tested, and well-documented Python code that can be reused by teammates.
• Participate in code reviews and technical discussions, explaining results to both technical and non-technical colleagues.
• Stay updated on current AI research, reproduce promising methods, propose experiments, and share learned insights with the team.
• Contribute to various team needs, as the team is small and collaborative.

Requirements & Tech Stack

• Proficiency in Python.
• Experience with at least one deep learning framework.
• Working knowledge of ML fundamentals, including training and validation, overfitting, and evaluation metrics.
• Hands-on experience with modern deep learning beyond off-the-shelf usage, including training or substantially adapting models.
• Comfort with Git, the command line, and working on a shared codebase.
• Clear writing and communication skills.
• Curiosity and a proactive approach to investigating issues in data or code.

Qualifications

• Master's degree or PhD in computer science, engineering, mathematics, or a related field, OR equivalent experience demonstrating comparable machine learning skills.
• Up to about 3 years of hands-on ML experience is a plus, including graduate research, thesis, internships, and industry work.
• Ability to explain design choices and tradeoffs for model adaptations.

Extra Points (Not Required)

• Hands-on experience with self-supervised, contrastive, or masked-modeling methods.
• Experience with large-scale training (multi-GPU runs or experiment tracking with tools like 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.
• Exposure to healthcare, medical device software, or other regulated settings.