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Overview We are looking for a Senior Computer Vision Engineer to build and deploy perception systems that run onboard autonomous aerial platforms. This is not offline research. Our algorithms run in real time on embedded computers, in the air, in conditions we do not control. You will work across the full stack of the problem: classical computer vision and deep learning, camera and sensor selection, calibration, model training and evaluation, and optimization for constrained hardware. You will take your work to the field, fly it, look at the logs, and improve it. Our platforms serve both commercial and government customers across applications including inspection, mapping, search and rescue and security. If you like hard real world problems and short feedback loops between an idea and a flight test, we want to talk with you. Responsibilities and Duties Design, implement and optimize onboard perception algorithms for real time detection, temporal association and state estimation of dynamic objects from a moving platform. Train, retrain and evaluate neural networks for detection and classification, including dataset curation, labelling strategy and evaluation methodology. Optimize models and pipelines for embedded inference, working to explicit latency, throughput and compute budgets. Develop and maintain camera calibration and sensor synchronization procedures, including multi camera and multi sensor setups. Combine visual measurements with inertial and platform state data using filtering and estimation techniques for robust performance under egomotion. Plan and run field test campaigns. Take algorithms from bench to flight, work with pilots and integrators on site, and close the loop on real captured data. Build repeatable evaluation methods so that performance and accuracy are measurable across every iteration rather than judged by eye. Provide input into camera, lens, sensor and compute selection, and evaluate hardware options against real performance data. Take full ownership of computer vision deliverables. Disambiguate requirements, drive alignment across teams, keep stakeholders informed and see work through to the customer. Mentor and coach other engineers, and raise the bar on product quality. Qualifications and Skills Degree in Computer Science, Electrical Engineering, Robotics or a related field, or equivalent practical experience. Strong hands on experience deploying computer vision systems on resource constrained platforms, ideally robotic or aerial. Solid grounding in classical computer vision: feature detection and matching, optical flow, camera models and calibration, multiple view geometry. Practical experience training and deploying convolutional neural networks for detection or classification, including the unglamorous parts: data collection, labelling quality, retraining loops and honest evaluation. Rigor about timing and synchronization across sensors. Understanding of why timestamps, exposure timing and latency matter, and experience debugging problems that turned out to be timing problems. Proficiency in C++ and Python, with real experience in OpenCV. Track record of taking computer vision work end to end, from problem definition to something running in a real deployment. Comfort with field work, including travel to test sites and debugging from real world logs and video. Ability to navigate ambiguity and lead across teams in a fast moving environment. Effective communication and collaboration across engineering, product and external stakeholders. Nice to have State estimation and sensor fusion. Kalman and extended Kalman filters, IMU integration, estimation under egomotion. Experience with visual inertial odometry or visual SLAM, or other hard estimation work on a moving platform. Experience with ROS2 or similar middleware. Experience with embedded inference runtimes and model optimization such as quantization or pruning. Familiarity with GPU or NPU accelerated pipelines on embedded SoCs. Experience building CI or hardware in the loop infrastructure for perception regression testing. Background in aerial platforms, PX4 or MAVLink based systems. Benefits Flexible working hours Stock Options Generous holiday allowance Company pension plan Car parking Enhanced maternity & paternity leave Mental health and wellbeing support Learning and development opportunities About Auterion We are building the leading software platform for AI-powered autonomous systems for enterprise and government customers. Our team is supporting the most important missions and solving the most challenging problems. At Auterion, we bring together the sharpest minds who love to solve the hardest problems. Join a vibrant team built on innovation, openness, and excellence, where you’ll be empowered to develop ideas, collaborate across all levels, and make an impact that matters.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.