[Hiring] Machine Learning Engineer, Applied @Trace

Role Description

This role sits between ML engineering and research. You’ll bring strong ML engineering skills, a research mindset, and deep learning fundamentals to the hardest problems we see coming at Trace, then ship the answers into production.

You’ll work closely with our computer vision team, but this role is broader and more product-focused. You won’t live on one type of data. One month you might be working with sensor signals, the next with hand tracking video, the next with language. What matters is getting a high-quality model working fast and putting it to use.

We care much more about what you’ve built and shipped than where you’ve published. If you’re earlier in your career but have a clear track record of moving fast and owning hard problems, we want to talk.

What you will do

• 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.

Qualifications

• A BS in Computer Science, Electrical Engineering, Mathematics, Aerospace, or a related field, or equivalent practical experience. An MS is a plus, not a must.
• 2+ years of industry 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.

Benefits

• A foundational problem. The data layer for physical AI is still being defined. You’ll help define it.
• Data at scale from day one. We have the resources to collect it. Your job is to make it count.
• Real ownership. Your models ship into production and directly shape what our data can do.
• Room to grow. Go deeper into ML, take on bigger systems, or both.