Machine Learning Engineer, Applied

Trace Labs is building data infrastructure for physical AI and developing machine learning systems for real-world training data. The Applied Machine Learning Engineer will own models end to end, build pipelines for large multimodal datasets, collaborate with computer vision and engineering teams, and deploy models into production.

Responsibilities:

• Own models end to end: data, training, evaluation, and deployment into our annotation pipeline
• Build training pipelines for large, multimodal datasets, including video, sensor streams, and language
• Partner with our Head of Engineering and the CV team to get models into production and keep them improving
• Start with the simplest approach that works, then make it better. Pull from recent papers when they help you ship, not as the end goal
• Move quickly between very different problems, and build the tooling you need along the way

Requirements:

• A BS in Computer Science, Electrical Engineering, Mathematics, Aerospace, or a related field, or equivalent practical experience
• 2+ years of industry of hands-on experience training deep learning models on real-world data, at a company, startup, or national lab
• Earlier in your career is okay if your track record shows it
• Strong proficiency in Python and a modern deep learning framework such as PyTorch or JAX
• A solid grounding in ML fundamentals, including architectures, optimization, loss design, and evaluation
• Comfort with messy, real-world data such as video, time series, or sensor streams
• An extraordinary bias toward shipping. You go from idea to working model fast, and you make good calls under uncertainty
• High agency. You find the problem, own it, and get it done without waiting to be told
• Range. You're excited to work across different kinds of data and problems, not just one narrow specialty
• An MS is a plus, not a must