Hardware Engineer

Tristar AI is a cutting-edge computer vision startup transforming quality control and safety in manufacturing through scalable, AI-driven defect detection systems. Our technology is deployed on high-speed lines across industries, delivering ROI through accuracy, efficiency, and automation.

Must be based in the Boston area and be willing to commute to our Cambridge, MA office at least 3 times a week.

We are seeking an experienced Hardware Engineer to support the design, configuration, and deployment of hardware setups for vision systems on a project-by-project basis. This role is ideal for someone with a deep understanding of industrial imaging, lighting, GPUs, and edge compute who can assess customer environments and help recommend, source, and validate optimal hardware configurations tailored to specific manufacturing use cases.

• Collaborate with Tristar AI’s engineering and deployment teams to define and design vision system hardware requirements based on customer use cases (e.g., lighting, camera specs, GPU and edge server requirements).
• Evaluate environmental and operational factors such as part speed, ambient lighting, space constraints, and mounting options.
• Recommend optimal hardware configurations including industrial cameras, lenses, lighting systems, compute hardware (e.g., NVIDIA GPUs), and networking equipment.
• Assist with testing and qualifying hardware for performance and reliability in pilot and production environments.
• Document configuration recommendations, wiring diagrams, and setup guides to support future deployments.

Participate in customer site visits and hardware validation efforts as needed.

• 5+ years of experience in hardware engineering, systems integration, or solution architecture involving machine vision systems.
• Strong working knowledge of industrial cameras (e.g., FLIR, Basler), lenses, lighting systems, and image acquisition hardware.
• Familiarity with NVIDIA GPUs, edge computing devices, and hardware integration for AI inference.
• Ability to think critically about environmental constraints and optimize hardware setups for real-world manufacturing conditions.
• Experience collaborating with cross‑functional technical teams and field engineers.
• Excellent written and verbal communication skills.

• Experience with defect detection, safety, or ergonomics‑related computer vision deployments.
• Exposure to manufacturing automation systems, PLCs, or industrial network protocols.