Applied ML Engineer — End-to-End Production Models

Openkrill; United States; À distance; USD 120 000 - 180 000; Plein temps

Résumé du poste

Trace Labs is building the data infrastructure for physical AI. This role sits between ML engineering and research, bringing a strong ML engineering skill set, a research mindset, and deep learning fundamentals to deliver high-quality models into production.

You will collaborate with the computer vision team on a broad range of data types, from sensor signals to hand tracking video and language, focusing on getting usable models fast and iterating rapidly based on results.

Qualifications

• BS in Computer Science, Electrical Engineering, Mathematics, Aerospace, or related field (or equivalent).
• 2+ years of hands-on experience training deep learning models on real-world data.
• Strong proficiency in Python and a modern deep learning framework (PyTorch or JAX).
• Solid ML fundamentals: architectures, optimization, loss design, evaluation.
• Comfort with messy real-world data like video, time series, or sensor streams.

Responsabilités

• Own models end-to-end: data, training, evaluation, deployment into the annotation pipeline.
• Build training pipelines for large, multimodal datasets (video, sensor streams, language).
• Collaborate with Head of Engineering and CV team to deploy models to production and improve.
• Start simple, ship quickly, then improve using recent papers when helpful.
• Move across diverse problems and build necessary tooling along the way.

Connaissances

Python

PyTorch

JAX

ML fundamentals

Data handling

Research mindset

Formation

Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Aerospace, or related field

MS is a plus

Description du poste

Trace Labs is building the data infrastructure for physical AI. This role sits between ML engineering and research, bringing a strong ML engineering skill set, a research mindset, and deep learning fundamentals to deliver high-quality models into production.

You will collaborate with the computer vision team on a broad range of data types, from sensor signals to hand tracking video and language, focusing on getting usable models fast and iterating rapidly based on results.