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We are looking for a highly hands-on Senior Computer Vision / DeepStream Team Lead to take technical ownership of our real-time vision pipeline running on NVIDIA Jetson devices.
This is first and foremost a role for someone who has already built and launched production-grade machine vision systems on Jetson + DeepStream.
The leadership part matters, but the core of the role is deep practical expertise: someone who can go into the pipeline, understand what is happening frame by frame, improve robustness and performance, and guide a small team from real technical strength.
You will lead a team of engineers while being the strongest technical anchor in the domain.
About the role:
Our systems operate in real-world logistics environments and process live video streams at the edge. The role includes ownership of a complex pipeline involving detection, tracking, decoding, event logic, integration with surrounding services, and deployment on embedded NVIDIA hardware.
We are looking for someone who knows what it takes to make these systems work outside the lab — under performance constraints, with real cameras, real customers, real failure modes, and real delivery pressure.
Key Responsibilities:
Technical Ownership
Own the architecture, quality and evolution of our edge vision pipeline running on Jetson + DeepStream.
Work directly in the code on performance, stability, detection flow, tracking, decoding logic, pipeline design, debugging and production readiness.
Drive continuous improvement of real-time video processing pipelines, including model integration, tracking, event generation, message flow, and interaction with surrounding system components.
Take features from concept to production in customer-facing products, with strong attention to robustness, maintainability and field performance.
Lead a small team of engineers across software and computer vision related areas, helping prioritise work, review design decisions, unblock execution and maintain high engineering standards.
Work closely with product, systems, QA and field teams to ensure the solution is practical, testable and deployable in real operational environments.
Requirements:
Must-have experience
Proven hands-on experience building and launching production systems on NVIDIA Jetson and DeepStream
Strong experience in real-time computer vision / video analytics
Strong practical experience with C++ and Python
Experience working on end-to-end edge pipelines, not just training models
Experience with tracking, detection pipelines, event logic, and system-level debugging
Experience integrating models and CV logic into production video pipelines
Strong understanding of performance optimisation on embedded NVIDIA platforms
Experience debugging real-world issues such as latency, dropped frames, synchronisation problems, memory/resource constraints and long-run stability
Ability to take ownership of a working but evolving system and improve it pragmatically
Leadership
Experience leading or mentoring a small engineering team
Able to combine hands-on technical execution with ownership, prioritisation and decision-making
Comfortable leading by doing, not only by coordinating
Advantage:
Experience with logistics, warehouse automation, robotics, industrial vision or similar operational environments
Experience with GStreamer, TensorRT, Triton, MQTT or similar ecosystem components
Experience with marker-based vision, decoding pipelines, geometry-based logic, or custom vision codes
Experience deploying and supporting systems in the field
M.Sc. / Ph.D. in Computer Science, Electrical Engineering or related field
What success looks like in this role
You quickly understand our existing pipeline and become the strongest technical owner of it
You improve system robustness, performance and maintainability on Jetson devices
You help shape the next generation of our decoding, tracking and event-generation logic
You raise the technical level of the team while still delivering yourself
You help turn a strong technical foundation into reliable customer-facing products
Why join us?
We are building real-world computer vision products for logistics environments, where the challenge is not just model accuracy but making full systems work reliably in production.
This role is an opportunity to lead from the front: to take ownership of a meaningful machine vision stack, work on difficult real-time problems on embedded hardware, and directly influence product delivery.
