Machine Learning Engineer, Applied

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $185k - $245k / year

⏰ Full Time

🟢 Junior

🟡 Mid-level

🤖 Machine Learning Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Trace

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2025

🤖 Artificial Intelligence

🏪 Marketplace

☁️ SaaS

Artificial Intelligence • Marketplace • SaaS

Trace is a data marketplace and infrastructure company that captures and prepares real-world data from humans doing physical work to create training datasets for robotics, embodied AI, and other physical-world AI systems. The company builds the supply network, operational infrastructure, and data workflows to capture, transform, and deliver high-quality, multi-sensor datasets and a marketplace that helps customers capture better data, iterate faster, and scale physical AI solutions.

📋 Description

• 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

• 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

• 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