Vision Systems Engineer

Job description | Trener Robotics

About us

At Trener Robotics, we are building the future of industrial automation through intelligent agents that understand natural language and control robotic stations. Our product, Acteris, bridges AI and robotics to deliver flexible, user-friendly automation.

We are a fast-growing, venture-backed company entering a phase of rapid scale. Our mission is to make AI-powered robotics accessible, and we are building the team that will make it real. Now, we are looking for a Vision Systems Engineer to join us and build the vision stack of Acteris.

The role

You will lead the technical development of the vision stack of our robotics software platform, Acteris, from sensor selection and integration to calibration, vision algorithms and quality in production. This is a hands-on role with high autonomy: you will drive the key technical decisions in the vision domain and be accountable end to end for the reliability of what ships. You will shape the vision roadmap together with product and engineering leadership.

What you will do

• Sensor selection and qualification: translate application needs into sensor specs, evaluate industrial 2D/3D cameras against factory constraints (performance, interfaces, ruggedness, IP rating, cost, 24/7 operation) and run bench and in-cell tests to a documented go/no-go.

• Sensor integration: build production-grade camera adapters behind a vendor-agnostic interface that exposes sensor status, visual feeds and calibration validity to the rest of the stack.

• Sensor and image tuning: tune sensor parameters (e.g. exposure, white balance…) and condition images and point clouds so perception gets a stable input under changing conditions.

• Calibration: own intrinsic and extrinsic calibration end to end, including tooling, validation metrics that reflect real-world accuracy, and workflows that non-experts can run on site.

• Perception skills: implement, integrate, and maintain capabilities such as part localization and pose estimation, combining classical geometry and learning-based methods pragmatically.

• Quality: define and own vision benchmarks and quality gates for the lab and for production, with clear diagnostics and failure reporting.

• Deployment tooling: build the interfaces and tools integrators use to set up, calibrate and validate the vision stack in production.

Required experience

• 5 to 8 years of hands-on industrial computer vision experience, with systems deployed and running in production.

• Integration and configuration of industrial 3D / RGB-D sensors, including intrinsic and extrinsic calibration and sensor tuning for image and depth quality.

• Solid 2D and 3D processing: depth maps, point clouds, filtering, geometric fitting and segmentation.

• Production-quality C++ and Python, with testing, code review and CI as part of daily work.

• Ownership and clear communication in a distributed team: you plan your work from goals, flag blockers early and report status openly.

Nice to have

• Experience with ROS 2 and behavior trees.

• Selecting and qualifying sensors for factory deployments.

• Deep learning or foundation models for segmentation or pose estimation, taken to production.

• Industrial camera interfaces and synchronization (e.g. GigE Vision, triggering, PTP).

• Vision-guided robotics, e.g. part picking, tray or bin picking, or machine tending and other production cells.

What you will get

• Direct impact on the vision layer of a category-defining automation platform, and on product reliability and customer outcomes.

• The opportunity to work hands-on with real robots from day one.

• A seat where engineering meets product: your work directly shapes how the platform performs in production.

• Collaboration with a high-caliber team across AI, robotics, systems engineering, and product, all aligned on a clear and ambitious mission.

• A fast-moving, mission-driven environment where rigorous engineering and pragmatic problem-solving are equally valued.

• A clear growth path as the vision domain scales.