Machine Learning Engineer, Applied

Trace Labs; United States; Remote; USD 100,000 - 180,000; Full time

Job summary

Trace Labs is building the data infrastructure for physical AI and robotics. We seek a Machine Learning Engineer to own end-to-end model lifecycles, from data collection and annotation through training, evaluation and deployment in production systems.

You will collaborate with the engineering and computer vision teams to integrate models and continuously improve them using real-world data and scalable pipelines. Strong Python and DL framework experience is essential.

Qualifications

• Strong Python proficiency and software development experience.
• Experience building end-to-end ML pipelines.
• Experience with real-world data for model training and evaluation.
• Background in robotics data is a plus.

Responsibilities

• Prepare data for training and annotation.
• Train, validate, and evaluate ML models.
• Deploy models to production systems.
• Collaborate with CV and software engineers to integrate models.

Skills

Python

Deep learning

Model training

Data pipelines

ML deployment

Education

BS in CS/related field

Tools

TensorFlow

PyTorch

Keras

Scikit-learn

Job description

Trace Labs is developing the data infrastructure for physical AI. The Machine Learning Engineer will manage models from data collection to deployment, focusing on real-world training data. Candidates with a strong background in Python and deep learning frameworks should consider applying.

At Trace Labs, the focus is on building scalable data infrastructure to support physical AI applications, such as robotics. The team seeks to overcome the current limitations in collecting high-quality real-world training data, which is essential for training frontier robotics models effectively.

In this role, you will be responsible for the end-to-end management of machine learning models, including data preparation, training, evaluation, and deployment within the annotation pipeline. Collaboration with the engineering and computer vision teams will be crucial to ensure models are effectively integrated and continuously improved.