*Please note this is an onsite role. It will be based out of our Brooklyn Navy Yard HQ or El Segundo office 5x a week. **What you'll do:** - **Embed on-site with design partners and manufacturers** for meaningful stretches of the year - their floor is your second office. - **Build trust across the full vertical**, from the GM to the technician running the station. Software and hardware deployed without this trust doesn't get adopted. - **Develop genuine understanding** of how each partner's shop works and why - the history, constraints, people, and physics of what moves through it. - **Build models on factory data**. You'll be extracting reliable signal from sparse, messy data, and need to know when regression beats a neural net. You'll ship ML that touches hardware: vision for inspection, part identification, document capture and parsing, models over sensor and machine data, and systems that run on the floor. - **Quantify the connection between operational areas for improvement and EBITDA**: which changes on the floor move throughput, quality, margin, business development, and by how much. - **Translate unstructured observation into concrete requirements**, then build, ship, and maintain the software yourself and through the other technical staff you coordinate on-site and at HQ. - **Write production code across the full stack** - data model, ETL, backend, frontend, algorithms, hardware integrations, ML pipelines, analyses, and infrastructure. Everyone wears all the hats at various points in time. - **Run the process-engineering side of deployment** - sequencing software changes with operational changes so the two evolve in lockstep, including the cultural work: training, change management, work instructions, quiet one-on-ones with skeptics. - **Own outcomes, not deliverables.** Success is measured in operational maturity and financial outcomes gained by the partner, not features shipped. **Who you are:** - 2+ years of experience in machine learning and software engineering in a fast and collaborative production environment - Statistical depth and intuition for small data: classical statistics, Bayesian methods, uncertainty quantification. - Experience taking models from notebook to production: feature pipelines, evaluation, monitoring, and retraining - Experience with computer vision, time-series and sensor data, causal inference or econometrics, optimization, operations research, applied math, data modeling, ETL, hardware integration, or system and API design - Strong communication/stakeholder management skills - Knowledge and intuition for designing and implementing data intensive applications - Experience with data modeling, ETL, system and API design, frontend, algorithms, hardware integration, data science, applied math, operations research, statistics, machine learning, devOps, or infrastructure - Hard-tech, robotics, and manufacturing background is a big plus **What we have:** - Medical, Dental, Vision benefits - Unlimited PTO Policy - $180-$250k + equity **Nice to Have** - Graduate education in computer science, statistics, applied mathematics, applied physics, economics, electrical engineering, mechanical engineering, or a related field - Experience in economics or econometrics is a big plus - ML work with a vision or hardware component, or in robotics-adjacent domains, is a big plus - Shipped ML systems or public work we can look at: products, papers, open-source, or write-ups - Track record leading technical projects, especially in a customer-facing role - Previous startup experience