Bucket Robotics

Role Overview Bucket Robotics is hiring a Forward Deploy Engineer to work at the boundary between our core engineering team and real manufacturing deployments. You’ll spend most of your time in San Francisco building alongside our engineers—but regularly travel to customer sites across the U.S. to deploy, debug, and harden our system in real production environments. This role is ideal for an engineer who wants to see their work leave the laptop and show up on factory floors—integrating cameras, models, and workflows with real constraints, real operators, and real uptime requirements. Responsibilities Deploy Bucket’s vision systems at customer manufacturing sites Work directly with customers during onboarding, pilots, and early production runs Integrate cameras, edge devices, and models into real production workflows Debug issues spanning software, ML models, hardware, lighting, and environment Serve as the technical owner for in-field issues from discovery through resolution Collaborate closely with the core engineering team to reproduce issues and ship fixes Translate real-world deployment pain points into actionable product and platform improvements Build deployment playbooks, checklists, and internal tooling to make future rollouts faster and more reliable Support technical evaluations alongside sales and leadership without being a sales role This role will require travel for up to 30% of the time. You’ll be hands-on helping customers use our technology, and when in SF building alongside the rest f our engineering team. Required Qualifications 2+ years experience in software engineering, robotics, systems, or field-facing engineering roles Strong programming skills in Python Comfortable debugging complex systems across hardware and software boundaries Experience working directly with customers in technical environments Enjoys wearing many hats and operating in fast-moving, ambiguous situations Willingness to travel ~30% of the time to customer sites across the U.S. Preferred Qualifications Experience deploying or supporting ML or computer vision systems in production Familiarity with cameras, edge devices, or embedded systems Background in robotics, manufacturing, automation, or industrial environments Experience with Linux-based systems and on-prem or edge deployments Previous startup or early-stage company experience What We Offer Competitive salary & meaningful equity – help shape how our technology works in the real world Comprehensive benefits – health, dental, and vision insurance Professional development budget – conferences, courses, and skills growth A chance to build something real – see your work running on factory floors across the country Our Values Innovation First: We solve hard, real-world problems in robotics and manufacturing Ownership Mentality: We take responsibility from deployment through resolution Customer Trust: We build systems customers rely on in production Continuous Learning: Every deployment makes the product better Bucket Robotics is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Role Overview Bucket Robotics is hiring a Machine Learning Engineer to push the frontier of CAD-native computer vision for manufacturing. You’ll work on the core ML systems that turn 3D geometry and synthetic data into reliable, production-grade vision models deployed on factory floors. This role is perfect for someone who gets genuinely excited about new research ideas, enjoys deep analytical thinking, and wants to see those ideas survive contact with reality—edge deployment, distribution shift, weird lighting, and all. You’ll operate at the boundary between research and production: exploring new approaches, validating them rigorously, and shipping the ones that work. Responsibilities Design, train, and evaluate computer vision and ML models for inspection and perception Develop novel approaches for learning from CAD, synthetic data, and limited real-world data Run rigorous experiments to understand model behavior, failure modes, and tradeoffs Improve model robustness across lighting, materials, viewpoints, and manufacturing variation Optimize models for edge deployment (latency, memory, reliability) Collaborate closely with engineering to integrate models into production systems Build evaluation frameworks and metrics that reflect real-world manufacturing requirements Stay current with cutting-edge research and proactively test promising ideas Required Qualifications 3+ years experience developing ML systems in research or production environments Strong foundation in machine learning fundamentals and statistical reasoning Hands-on experience training deep learning models (e.g. PyTorch) Strong Python skills and comfort working in experimental codebases Enjoys deep problem-solving, experimentation, and analytical thinking Comfortable operating in ambiguity and iterating quickly Preferred Qualifications Experience in computer vision, 3D vision, or geometry-aware ML Familiarity with synthetic data generation, simulation, or domain randomization Experience deploying models to edge or resource-constrained environments Background in robotics, manufacturing, or physical-world ML systems Authored technical reports, blog posts, or peer-reviewed research Previous startup or early-stage company experience What We Offer Competitive salary & meaningful equity – help define the core ML systems of the company Comprehensive benefits – health, dental, and vision insurance Professional development budget – conferences, courses, and research exploration A chance to work on hard problems – ML that has to work in the real world, not just benchmarks Our Values Innovation First: We push the boundaries of what's possible in ML and advanced manufacturing Ownership Mentality: We take responsibility and act like founders Collaborative Spirit: We work together to solve complex challenges Continuous Learning: We're always growing and improving Bucket Robotics is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

What we do Bucket Robotics is the trust layer for physical production. We turn CAD files into production-ready computer vision models that detect defects instantly with zero manual labeling and no multi-month pilots. While the industry has struggled for decades with fragile rule-based vision systems, we use high-fidelity synthetic data to simulate every possible defect and lighting condition before the part even hits the line. We help factories ship better products starting on day one. We believe manufacturing knowledge should be portable, inspectable, and reproducible across factories. How we work These aren't aspirations. They're how we make decisions when no one's looking. Be a builder. Build systems, relationships, knowledge, and the company itself. We hire people who create, hack, improve, and refine. We look for those who act with high agency when the path isn't drawn yet. Ambiguity is the default state of an early company; treat it as raw material rather than a problem someone else needs to solve first. Generalists compound. Our product is vertically integrated, spanning CAD ingestion, synthetic data generation, model training, edge deployment, customer-facing tooling, and ops. There is infinite depth in every layer. The opportunity here isn't to know a little of everything; it is to be a generalist who keeps going deeper across a stack that won't run out of interesting problems for the next decade. Bias toward the physical. Get on planes. Stand next to the machine. Watch the operator use the thing. The best product instincts in this company come from a factory floor rather than a Figma. Velocity is a moat. We experiment before we discuss. A thirty-minute meeting about whether a thing will work usually loses to a thirty-minute prototype that just tries it. We prototype aggressively. LLMs, scaffolding, and rough drafts help us skip the parts that do not deserve perfection yet, so we can spend real craftsmanship where it matters. This ensures we have time for the parts that do. Write the ontology before the code. Names matter. Schemas matter. A clean data model saves a year of refactors. We argue about nouns on purpose. No heroes, no martyrs. Sustainable urgency, not crunch theater. Take the weekend. Then come back and ship. The role You will be one of the first fullstack hires. That means you own product surfaces end-to-end, including the schema, API, UI, deployment, and being on-call when it breaks. You will work next to the ML and robotics engineers, but you are the person customers actually interact with every day. You'll spend your time on things like: The customer-facing app where engineers upload CAD, configure models, and watch their lines. The internal tools our deployment engineers use to get a customer from the first call to running in production in days instead of quarters. The data plumbing that turns synthetic runs, real captures, and production telemetry into something we can reason about. Early-stage companies create constantly shifting priorities. We value people who can navigate ambiguity without losing momentum. Stack: Ruby on Rails, ERB, React, Postgres. We chose boring tools on purpose. We'd rather spend our novelty budget on the vision models. Who you are Rockstar? Pass. Thoughtful? Let's talk. We hire eager learners, conscientious workers, and kind, supportive humans. We look for people who set their own priorities, ask their own questions, and ship without being managed into it. We don't weight where you went to school, where you worked before, whether you graduated or dropped out, or how decorated the resume looks. We care about who you are today, how you'll be tomorrow, and what you can do this week. On top of that, this particular role needs: Roughly five years shipping fullstack web software at a production level. Opinions about data models and the ability to defend them without an ego. Comfort across the whole stack: schema, API, React, deploy. No handoffs to a teammate to do your job. E