AI Applications Engineer (Boston)

Standard Bots•Boston, MA

19h•$120,000 - $150,000•Onsite

About The Position

The AI Applications Engineer sits at the intersection of robotics, AI, and hands-on field work. You're not a pure software engineer and you're not a pure hardware engineer -- you're the person who can look at a manufacturing challenge, figure out how computer vision or a language model makes the robot smarter, and then actually build and deploy the solution. You'll work closely with the sales and applications engineering teams to scope AI-enabled solutions, build proofs of concept in our shop, and travel to customer sites to install and commission them. You'll own your projects end-to-end -- from initial design through field deployment and customer training. This is a builder's role. Tinkerers and people who get bored at a desk are strongly encouraged to apply. Note: This role requires up to 50% domestic travel. That number goes down as we grow the team, but right now you're in the field -- a lot. If that's not for you, this probably isn't the right fit. Also note: If you're looking for a role where you train models and push code, this isn't it. This is a mechanical + electrical + software trifecta that happens to use AI as a tool. The factory floor is your office.

Requirements

Robot programming proficiency or adjacent software experience -- if you can write Python without an AI agent, you can probably program a robot having never touched one before
Prior robotics knowledge -- anything from high school robotics to professional manipulation experience; we care that you've actually worked with robots, not just read about them
Surface-level fluency in ML models and techniques -- you can have a real conversation about convolutional neural networks, object detection, and visual language models without Googling the definitions mid-sentence
Technical proficiency in an engineering environment -- you write documentation, manage your own projects, work well with a team, and don't need someone watching over your shoulder
Willingness to travel -- up to 50% domestically right now; that number will come down as we grow

Nice To Haves

Mechanical Design -- pneumatics, camera mounts, end-of-arm tooling for manipulators; bonus points if you've designed a gripper
Electrical Design -- custom board fabrication, general electrical knowledge for wiring relays and solenoids in automation environments
Machine Vision -- you know which cameras and lenses to use for a given application and why; you've actually deployed a vision system, not just selected one on paper
Experience with collaborative robots (UR, Fanuc, KUKA, ABB, or similar)
Hands-on work with vision systems in a production or lab environment (Cognex, Basler, FLIR, or similar)
Familiarity with ROS or other robotics middleware
Background in a manufacturing or industrial automation environment
Experience at a startup -- you know what it means to build something from scratch with limited resources

Responsibilities

Work with customers and the sales team to identify where AI -- computer vision, object detection, visual language models -- creates real leverage in their production process
Design and build proofs of concept that validate the solution before anyone signs anything
Develop documentation, sample code, and integration guides that help customers and internal teams replicate and scale what you've built
Travel to customer sites to install, configure, and commission AI-enabled robotic cells
Own the full integration: camera placement and calibration, model deployment, I/O configuration, testing, and sign-off
Troubleshoot on-site when things don't go as planned -- you stay until it works
Train customer operators and engineers on the AI capabilities of their Standard Bots deployment
Be a trusted technical resource post-install as customers push the system into new applications
Feed real-world deployment learnings back to our engineering and product teams
Work with the broader applications and engineering teams on new feature testing, peripheral integrations, and platform expansion
Help build the internal knowledge base on AI-enabled applications -- what works, what doesn't, and why