Machine Learning Engineer, Applied

Trace

. Own models end to end: data, training, evaluation, and deployment into the annotation pipeline .

Posted 10/8/2026full-timeRemote • United StatesJuniorMid-Level💰 $185,000 - $245,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying deep learning models, with a strong foundation in machine learning fundamentals and proficiency in Python and modern frameworks like PyTorch or JAX. Capable of handling complex, multimodal datasets and delivering solutions in production environments.

Highest-signal resume keywords

Deep Learning Model TrainingPython ProficiencyPyTorch FrameworkMachine Learning FundamentalsMultimodal Data Handling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningMachine LearningModel EvaluationData TrainingOptimizationLoss DesignData Pipeline DevelopmentComputer VisionSensor Stream ProcessingTime Series Analysis

Soft Skills

High AgencyProblem-SolvingAdaptabilityCollaboration

Industry Keywords

Computer ScienceElectrical EngineeringMathematicsAerospaceReal-World Data

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Own models end to end: data, training, evaluation, and deployment into the annotation pipeline
Build training pipelines for large, multimodal datasets, including video, sensor streams, and language
Partner with the Head of Engineering and computer vision team to get models into production and keep them improving
Start with the simplest approach that works, then improve it using recent papers when useful
Move quickly between different problems and build the necessary tooling
Work across ML engineering and research on difficult problems and ship solutions into production

Requirements

What you’ll need

A BS in Computer Science, Electrical Engineering, Mathematics, Aerospace, or a related field, or equivalent practical experience
2+ years of hands-on industry experience training deep learning models on real-world data
Strong proficiency in Python and a modern deep learning framework such as PyTorch or JAX
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
Extraordinary bias toward shipping
High agency
Range across different kinds of data and problems
An MS is a plus, not a must

Benefits

Comp & perks

0.1% – 1% equity
Room to grow: Go deeper into ML, take on bigger systems, or both
Real ownership: Models ship into production and directly shape what data can do
Data at scale from day one
Resources to collect data