--- title: 'Machine Learning Engineer, Applied at Trace' canonical: 'https://feeny.ai/job/machine-learning-engineer-applied-trace-united-states-f0kys1rjxtbs' type: 'job' last_seen: '2026-10-19' --- # Machine Learning Engineer, Applied at Trace - **Company:** Trace - **Location:** United States - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-10-07 - **Last confirmed live:** 2026-10-19 - **Apply:** https://jobs.ashbyhq.com/tracelabs/84f1786a-1f06-4798-892c-682a6ab07ba5 ## Job description ## About Trace Trace Labs is building the data infrastructure for physical AI. Physical AI has the potential to transform how work gets done in the real world, from robotics to embodied systems that can see, move, and interact with their environment. But today, progress is held back by one big gap: there’s no scalable way to collect high-quality, real-world training data. Frontier robotics models are trained on far less data than language models, because there’s no “internet of robotics data.” Trace exists to change that. We capture how humans actually interact with the physical world, at scale, and we build the ML systems that make every hour of that data more valuable. We’re an early, deeply technical team. We move fast, we care about quality, and we believe the teams with the best data will build the best robots. ## The role This role sits between ML engineering and research. You’ll bring strong ML engineering skills, a research mindset, and deep learning fundamentals to the hardest problems we see coming at Trace, then ship the answers into production. You’ll work closely with our computer vision team, but this role is broader and more product-focused. You won’t live on one type of data. One month you might be working with sensor signals, the next with hand tracking video, the next with language. What matters is getting a high-quality model working fast and putting it to use. We care much more about what you’ve built and shipped than where you’ve published. If you’re earlier in your career but have a clear track record of moving fast and owning hard problems, we want to talk. ## What you will do - Own models end to end: data, training, evaluation, and deployment into our annotation pipeline. - Build training pipelines for large, multimodal datasets, including video, sensor streams, and language. - Partner with our Head of Engineering and the CV team to get models into production and keep them improving. - Start with the simplest approach that works, then make it better. Pull from recent papers when they help you ship, not as the end goal. - Move quickly between very different problems, and build the tooling you need along the way. ## What we're looking for - A BS in Computer Science, Electrical Engineering, Mathematics, Aerospace, or a related field, or equivalent practical experience. An MS is a plus, not a must. - 2+ years of industry of hands-on experience training deep learning models on real-world data, at a company, startup, or national lab. Earlier in your career is okay if your track record shows it. - Strong proficiency in Python and a modern deep learning framework such as PyTorch or JAX. - A 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. - An extraordinary bias toward shipping. You go from idea to working model fast, and you make good calls under uncertainty. - High agency. You find the problem, own it, and get it done without waiting to be told. - Range. You’re excited to work across different kinds of data and problems, not just one narrow specialty. ## Why Trace Labs - A foundational problem. The data layer for physical AI is still being defined. You’ll help define it. - Data at scale from day one. We have the resources to collect it. Your job is to make it count. - Real ownership. Your models ship into production and directly shape what our data can do. - Room to grow. Go deeper into ML, take on bigger systems, or both. ## About Trace ## Company Overview - **One-liner**: Trace builds the data infrastructure and marketplace for physical AI, turning real-world human work into structured training datasets for robotics and embodied AI models. - **Entity Type**: Private (early-stage startup) - **Headquarters**: Not publicly disclosed; operates as a remote-first company (USA-based) - **Founded**: Not publicly disclosed - **Founders**: Not publicly named; described as "experienced founders with a successful exit" ## Core Business - Primary industry: Data infrastructure for robotics and physical AI (robotics training data, embodied AI datasets) - Target customers: Robotics companies, foundation model labs, and any organization building AI that operates in the physical world (B2B, enterprise) - Mission or purpose: To solve the core bottleneck of real-world training data for physical AI by creating a scalable, trusted marketplace and operating layer. ## Products & Services - **Trace Data Marketplace**: A platform that captures real-world data from humans performing physical work, transforms it into high-quality training datasets, and delivers it to customers across changing formats, sensors, and requirements. Type: SaaS + operational service. ## Market Standing - **Valuation/Market Cap**: Not disclosed (early-stage startup) - **Key Metric**: Total Funding – Not publicly disclosed; backed by "leading investors" and "top investors" according to the company website. - **Notable Investors/Partners**: Not named publicly; the company states it is backed by leading investors who understand the physical AI data infrastructure opportunity. - **Growth Signals**: Building a small, senior team; actively hiring for Full Stack and Computer Vision engineers; early-stage company with strong founder experience and a successful track record. ## Competitive Advantages - First-mover focus on a dedicated data marketplace for physical AI, addressing a critical bottleneck that lacks a scalable equivalent to the internet of data for language models. - Senior, tight-knit team with previous startup exit experience, enabling fast execution and high trust. - Platform designed to support multiple capture modalities and workflows, making it foundational infrastructure rather than a narrow dataset product. ## Strategic Focus - Scale the supply network and operational infrastructure to capture real-world data at volume. - Build long-term relationships with customers and adapt the platform as physical AI evolves. - Attract early engineers who can shape the product, systems, and company culture. ## Why Work Here - **Culture**: High trust, clear communication, and a high bar for quality in a lean, ambitious startup environment. - **Remote-first**: Work from anywhere (USA-based), flexible hours. - **Ownership**: Early employees have real impact on product direction and company values. - **Compensation**: Competitive salary and significant equity. - **Perks**: Generous vacation policy ("take time whenever you need to recharge"). ## Sources 1. [tracelabs.ai](https://tracelabs.ai/) – Company homepage and "Why Trace" narrative 2. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/tracelabs/963005ce-f4f1-4d54-957d-139b7849cb4a) – Full Stack Engineer job posting 3. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/tracelabs/774fbd47-20ce-4665-9bdb-5d3c0c68bc68) – Senior Computer Vision Engineer job posting 4. [workfindy.com](https://workfindy.com/jobs/0963bcf6-cc2e-4351-9b4b-ab4edb66ca08) – Trace Labs company summary and open roles 5. [tangerinefeed.net](https://tangerinefeed.net/job/-MPuU) – Senior Computer Vision Engineer role description ## Other roles at Trace - [Researcher](https://feeny.ai/job/researcher-trace-new-york-m7qf76y10rv2) — New York, NY