Applications Hardware Engineer

About Tristar AI:

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.

Role Overview:

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. Must have experience with optics and deploying cameras.

Key Responsibilities:

● Collaborate with Tristar AI’s engineering and deployment teams to define/design vision system hardware requirements based on customer use cases (e.g., lighting, camera specs, structural design, GPU/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. Qualifications:

● 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.

Preferred:

● Experience with defect detection, safety or ergonomics-related computer vision deployments.

● Exposure to manufacturing automation systems, PLCs, or industrial network protocols.