Applied ML Engineer: End-to-End Multimodal AI

Trace; New York (NY); On-site; USD 185,000 - 245,000; Full time

Job summary

Trace Labs is seeking a capable ML engineer with a research mindset to bridge ML engineering and research. You will drive end-to-end model development across varied data types, from data collection to deployment in production.

You will collaborate with the CV team to ship models quickly, leveraging PyTorch or JAX and strong ML fundamentals. The role emphasizes fast delivery and the ability to adapt to multiple data modalities.

Qualifications

• 2+ years of hands-on experience training deep learning models on real-world data.
• Proficient in Python with modern DL frameworks (PyTorch or JAX).
• Solid ML fundamentals: architectures, optimization, loss design, evaluation.
• Comfort with messy real-world data such as video, time series, or sensor streams.
• An extraordinary bias toward shipping and ownership of problems.

Responsibilities

• Own models end to end: data, training, evaluation, deployment into production.
• Build training pipelines for multimodal datasets (video, sensor streams, language).
• Collaborate with Head of Engineering and CV team to deploy and improve models.
• Move quickly between diverse problems and build needed tooling.

Skills

Python

ML engineering

Strong shipping bias

Research mindset

Education

Bachelor's degree in Computer Science or related

Tools

PyTorch

JAX

Job description

Trace Labs is seeking a capable ML engineer with a research mindset to bridge ML engineering and research. You will drive end-to-end model development across varied data types, from data collection to deployment in production.

You will collaborate with the CV team to ship models quickly, leveraging PyTorch or JAX and strong ML fundamentals. The role emphasizes fast delivery and the ability to adapt to multiple data modalities.