*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
