Jobs at Ambarella, Inc.

Lead product marketing and go-to-market strategy for edge AI solutions serving robotics, drones, machine vision, and industrial automation markets. Define hardware and software positioning, gather customer requirements, shape roadmaps, enable sales and FAE teams, grow partner and developer ecosystems, and support Ambarella’s Developer Zone. Manage cross-functional initiatives, technical demonstrations, events, customer relationships, and partner programs, with up to 30% international travel.

Develop and optimize classical computer vision and neural-network ISP acceleration pipelines on Ambarella’s CVflow architecture. Port algorithms and models to NVP and GVP processors, improving compute performance, memory efficiency, latency, power consumption, and real-time edge performance. Collaborate with algorithm, systems, and hardware teams on embedded computer vision applications involving image processing, optical flow, and radar/LiDAR point clouds.

Design, implement, and test imaging and video algorithms and DSP microcode for Ambarella SoCs. Develop firmware, research and implement DSP algorithms, optimize CPU and memory usage, create hardware-block test code, and debug assembly. The role requires understanding computer architecture, video standards, image processing, neural network frameworks, software development practices, and cross-functional teamwork.

Develop highly efficient deep learning and computer vision algorithms for edge applications, including self-driving cars and humanoid robots. Responsibilities include designing, training, and optimizing Transformer models, researching multimodal large language models and embodied AI, and building vision systems. The role also involves applying computer vision methods such as object detection, tracking, segmentation, and vision-language modeling, alongside robot control and sensor integration using ROS 2.

Develop software tools for future Ambarella embedded processors used in computer vision and intelligent automotive applications. Design and implement compiler language features, performance optimization tools, and related development capabilities. Collaborate with scientists, architects, and software and hardware teams to optimize chip performance. Work independently within a small R&D team.

Supports senior circuit designers in designing, validating, and characterizing mixed-signal and analog IP blocks for SoCs, including PLLs, DLLs, SERDES, high-speed I/O, and data converters. The role provides exposure to advanced sub-14nm technologies, analog integrated circuit design, digital logic, Verilog coding, laboratory work, and signal integrity.

Design and verify Design-for-Test logic for complex SoCs, automate scan, compression, and MBIST flows, analyze fault coverage, and perform timing analysis for DFT modes.

Develop and debug verification testbenches, tests, RTL logic, random test generators, coverage monitors, checkers, assertions, and simulation-analysis tools for complex SoCs featuring DSP, computer vision, CPU, and coprocessor blocks.

Design and verify ASIC logic for edge AI vision processors, including video compression, image processing, neural-network accelerators, and processor cores. Develop RTL in Verilog and SystemVerilog, optimize designs for timing, area, and power, and create UVM-based testbenches, BFMs, test generators, coverage monitors, and verification plans. Support synthesis, front-end tool flows, chip bring-up, and testing.

Implements and verifies design-for-test features, including scan, compression, MBIST, LBIST, and streaming scan networks, for complex computer vision SoCs. Develops scan and memory-test patterns, analyzes clocking for at-speed testing, performs post-layout timing verification, supports ATE pattern bring-up, and assists with silicon production, failure diagnosis, and test optimization.

Design, train, optimize, and validate deep learning and computer vision algorithms for edge devices. Implement and optimize models for runtime on Ambarella hardware and assess performance on large-scale datasets.

Test and validate optical and image-processing performance of cameras and sensors. Design and run multi-scenario test cases (indoor/outdoor), debug image quality with developers, evaluate results, produce test reports, and support product optimization, demos, and exhibition activities.

Develop, bring up, and verify embedded platforms and drivers in C/C++ on ARM/MIPS. Work with RT-Linux, lab equipment, and hardware teams to implement and test low-level control drivers and ensure system-level integration.

Develop, tune, and debug digital image/video processing software for cameras (Digital/Hybrid/IP/DV camcorders). Focus on image/video color and quality tuning, software debugging on PC, and cross‑team collaboration.

Design and verify video-compression, image-processing, vector-compute, and edge AI processor subsystems using SystemVerilog/Verilog. Synthesize and optimize RTL for timing, area, and power; prototype microarchitectures with scripting; develop unit tests, front-end flows, and coverage analysis; participate in chip bring-up and testing.

Install, maintain, and troubleshoot Linux servers and core infrastructure services (Kickstart, DNS, DHCP, NFS, LDAP, NIS). Administer Apache, manage container environments (Docker/Podman), implement security and patching, create documentation and runbooks, perform root cause analysis, and provide helpdesk-level technical support.

Work on computer vision algorithms for edge AI vision processors, extracting and processing high-resolution video data. Tasks likely include algorithm prototyping, evaluation, and performance optimization for camera-based edge applications while collaborating with engineering teams.

Develop and maintain Linux distributions and major system components for edge AI vision processors. Responsibilities include cross-compilation, software repository maintenance, and tool development, working within open-source communities and Linux desktop environments.

Design and implement system-level software for AI vision processors used in edge camera applications. Work on low-level drivers, performance optimization for high-resolution video pipelines, hardware/software integration, and reliability for real-time inference on edge devices. Collaborate with firmware, hardware, and application teams.

Support development and evaluation of computer vision and AI algorithms optimized for edge vision processors, processing high-resolution video streams. Implement prototypes, run experiments, profile performance, and collaborate with hardware and software teams to improve accuracy and efficiency.