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Description
Computer Vision Engineer — L3/L4
Hyderabad, India · Full-time · On-site with real robots
Give Robots the Eyes to Work
A robot cannot reliably pick, insert, assemble, or inspect what it cannot accurately see.
In a factory, vision is rarely clean: parts reflect light, cables occlude features, objects arrive in unexpected orientations, components vary between batches, and a few millimetres of pose error can turn a successful insertion into a failure. At Perceptyne, computer vision is not a separate analytics function—it is part of the real-time system that lets a robot understand its workspace and use its hands.
Perceptyne builds industrial humanoid robots that close the gap between robots that can do pre-programmed moves and robots that can do dexterous work: dual-arm, highly dexterous, contextually intelligent, and appropriately priced to deploy. We are building for electronics and automotive assembly, e-commerce packaging and more.
If you want to be a part of building the perception stack and build vision systems that directly determine whether a physical robot can grasp, align, insert, inspect, and recover from errors in the real world, this is that job.
What You’ll Actually Do
- You will own significant parts of the perception stack from raw sensor data to robot action. You will work closely with manipulation, controls, mechanical, electronics, and AI engineers—and validate your work on physical robot hardware.
- Design and deploy robust, real-time perception pipelines for manipulation and assembly tasks.
- Build deployment grade modular computer-vision code using / improving upon existing libraries for calibration, feature extraction, geometric matching, image enhancement, segmentation, tracking, metrology, and inspection.
- Develop methods for 2D/3D object detection, pose estimation, registration, grasp localization, alignment, and visual servoing.
- Work with RGB, stereo, depth, and industrial cameras; own practical aspects of imaging such as selection, camera placement, triggering, synchronization, exposure, lighting, calibration, and failure detection.
- Build sensor-fusion pipelines combining cameras, depth sensors, force / torque sensors, touch sensors robot kinematics, encoders, and other relevant signals.
- Implement robust image-processing techniques for difficult real-world scenes: glare, reflections, low texture, motion blur, occlusion, changing illumination, transparent or reflective surfaces, and part-to-part variation.
- Design data-collection and evaluation workflows, including ground-truth capture, dataset curation, test-case construction, metrics, error analysis, and regression testing.
- Integrate classical CV, geometric reasoning, and learned models where each is appropriate. Use deep learning when it improves the system
- Optimize pipelines for edge deployment, including latency, throughput, memory use, GPU/CPU utilization, and reliable production operation.
- Prototype in offline datasets and simulation, then validate results on real robots. The robot and the production environment are the final benchmark.
- Debug cross-functional failures where the root cause may be in optics, illumination, synchronization, calibration, sensor drivers, robot kinematics, coordinate transforms, inference, or a downstream planner.
What We’re Looking For
- This is a senior hands-on role for someone who can independently turn an ambiguous perception problem into a reliable system running on a robot.
- Bachelor’s, Master’s, or PhD in Computer Science, Electrical/Electronics, Mechanical Engineering, Robotics, Computer Vision, or an equivalent technical field.
- 5+ years of hands-on computer-vision engineering experience, including technical ownership of production or field-deployed vision systems.
- Strong C++ and Python skills. You should be comfortable writing performant production code, debugging native libraries, profiling bottlenecks, and working in Linux-based environments.
- Deep practical experience with traditional computer vision and image processing—not only deep-learning-based techniques.
- Strong working knowledge of OpenCV or equivalent production-grade CV libraries.
- Experience with techniques such as camera calibration, stereo geometry, image rectification, filtering, thresholding, morphology, edge and contour extraction, feature extraction/matching, template matching, optical flow, geometric transforms, tracking, image registration, and image-quality evaluation.
- Solid understanding of multi-view geometry, projective geometry, coordinate systems, transformations, camera models, and 3D reconstruction or pose estimation.
- Demonstrated ability to diagnose why a vision pipeline fails in real images and improve it through optics, lighting, data, algorithm design, calibration, or system integration.
- Experience deploying CV pipelines on edge compute, with attention to real-time latency, frame rate, determinism, reliability, and resource limits.
- Experience in robotics, industrial automation, machine vision, manufacturing inspection, bin picking, or assembly automation.
- Experience with industrial cameras and standards such as GigE Vision, USB3 Vision, synchronization and multi-camera systems.
- Experience with depth cameras, stereo vision, structured light, time-of-flight sensors, LiDAR, or point-cloud processing
- Grit and dedication: you stay with difficult, ambiguous technical problems through repeated failed experiments and work systematically until the system is robust—not merely successful in one controlled demo.
Modern AI + Traditional CV is the combo:
Modern AI is very valuable, but it is not the whole perception stack.
For some robotics tasks, the system needs precise geometry, explainable failure modes, reliable timing, and stable behaviour even when the available dataset is small or the part geometry changes.
We are specifically looking for engineers who can build and troubleshoot that full stack that integrates both Modern AI and traditional CV based methods.
You should be comfortable selecting an approach based on the problem:
Nice to Have:
These are valuable but not mandatory. Apply if you are strong in the core requirements even if you do not match every item below.
- Familiarity with PCL, Open3D, NVIDIA VPI, CUDA, models on Omniverse , TensorRT, or GStreamer
- Experience with robot-camera hand–eye calibration, visual servoing, eye-in-hand systems, or vision-guided manipulation.
- Knowledge of ROS/ROS 2, TF/coordinate-frame management, MoveIt, rosbag, RViz, Gazebo, Isaac Sim, or robot simulation.
- Experience with machine learning for perception, including CNNs, transformers, segmentation, representation learning, synthetic-data generation, or model deployment.
- Experience designing fixtures, lighting, optical setups, or test rigs for machine-vision systems.
- Contributions to relevant open-source projects, technical publications, deployed products, patents, or a portfolio of working CV systems.
How We Work
At Perceptyne, the robot is the referee.
A benchmark score, a successful offline result, or an impressive model architecture matters only when the robot can use it to complete a physical task reliably. We value first-principles reasoning, fast experimentation, direct observation, and disciplined engineering.
- We hold a few deliberate tensions:
- Deep experience without losing first-principles thinking.
- A large vision without losing attention to detail.
- Disruptive technology shipped to market as fast as possible.
Advanced AI where it helps, and simple, robust engineering where it works better.
You will thrive here if you build before you over-discuss, care about the last 5% of reliability, communicate clearly across mechanical, electrical, controls, and software teams, and take ownership from the first image through reliable deployment.
Skills
PythonLinuxMachine LearningDeep LearningComputer Vision