Senior Embedded AI Researcher
Nexar Inc.
- London, England, United Kingdom
- London, England, United Kingdom
Über
We are looking for a highly skilled engineer to bring our cutting‑edge computer vision models to life on Ambarella CVxx‑based dash cams, working closely with research engineers to optimize neural networks for object detection, video reconstruction, and collision prediction in a resource‑constrained embedded environment.
What We Offer
Meaningful impact on roads and city safety
Flexible schedule, work location, and benefits
Competitive compensation
Fast‑paced environment with smart, driven colleagues
Professional and personal growth opportunities
Team‑building events and well‑being initiatives
Who We Are Nexar turns cars into vision sensors to understand the world. Our platform powers vision‑connected services and apps at scale, driving autonomous vehicle and public sector projects worldwide.
Requirements
BS/MS/PhD in Computer Science, Electrical Engineering, or related field
3+ years of industry experience in embedded ML, edge AI, or related domains
Track record of shipping ML models in production embedded systems
Proficiency in C/C++ for embedded systems and performance‑critical code
Understanding of computer architecture: cache hierarchies, memory systems, SIMD/vector processing
Knowledge of DMA, interrupts, and hardware‑software interfaces
Experience with embedded AI platforms and hardware accelerators (e.g., Ambarella CVFlow, NVIDIA Jetson, Qualcomm Hexagon)
Deep understanding of computer architecture, kernel scheduling, I/O, memory management, GPU/NPU pipelines
Experience with heterogeneous computing (CPU/GPU/NPU/DSP coordination)
Prior experience deploying neural networks to resource‑constrained devices (mobile, automotive, IoT)
Professional experience with TensorFlow, PyTorch, or Caffe
Bonus Points
Direct experience with Ambarella SoCs and CVFlow architecture
Background in computer vision applications (object detection, segmentation, tracking)
Experience with automotive ADAS development and safety standards
Knowledge of video codec integration (H.264/H.265)
Understanding of fixed‑point arithmetic and numerical stability
Experience with TensorRT, LiteRT, ONNX Runtime, or similar inference engines
Contributions to ML deployment frameworks or toolchains
Responsibilities
Develop, optimize, and deploy deep learning models on Ambarella CVFlow‑based camera platforms
Translate state‑of‑the‑art AI models into production‑ready code, bridging research and systems teams
Deep‑dive into CVFlow architecture to maximize neural network performance
Profile model performance and address bottlenecks in I/O, memory, and compute
Implement model optimisation techniques: quantization, pruning, knowledge distillation, architecture search to meet latency and power budgets while maintaining accuracy targets
Develop custom operators and layers optimized for CVFlow’s vision processing engine
Collaborate with systems engineers to integrate AI models with camera firmware and software
Stay current with advances in embedded AI, GenAI, and computer vision
What Makes You a Good Fit
Excited about squeezing performance gains from carefully tuned operators
Can explain both ResNet architecture and L2 cache eviction policies
Think about problems both from model‑architectural and resource‑constraint perspectives
Enjoy the intersection of research innovation and engineering pragmatism
Motivated by the constraints of real‑time, power‑limited embedded systems
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Sprachkenntnisse
- English
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