Staff AI Engineer, Sonatus
Inference & Optimization
$197.5-260k
+ Stock option plan
3 days a week in office (Sunnyvale, CA)
Cloud-based solutions for software-defined vehicles
Be an early applicant
Cloud-based solutions for software-defined vehicles
101-200 employees
Be an early applicant
$197.5-260k
+ Stock option plan
3 days a week in office (Sunnyvale, CA)
101-200 employees
Company mission
To accelerate automotive innovation
Role
Who you are
- We're looking for a highly skilled and experienced Staff AI Engineer with domain expertise in optimizing AI models for production Edge environments
- Minimum 7 years of work experience in MLOps or a similar role with a strong focus on high-performance machine learning systems
- Proven experience with inference optimization techniques such as quantization (INT8, FP16), pruning, and model distillation
- Deep hands-on experience with hardware acceleration for machine learning, including familiarity with GPUs, TPUs, NPUs and related software ecosystems
- Strong experience with AI compilers and runtime environments like TensorRT, OpenVINO, and TVM
- Proven experience deploying and managing ML models on edge devices (e.g., NVIDIA Jetson, Raspberry Pi, NXP, Renesas)
- Strong experience in designing and building distributed systems. Proficiency with inter-process communication protocols like gRPC, message queuing systems like MQTT, and efficient data handling techniques such as buffering and callbacks
- Hands-on experience with popular ML frameworks such as PyTorch, TensorFlow, TFLite, and ONNX
- Proficiency in programming languages, including Python and C++
- Solid understanding of machine learning concepts, the ML development lifecycle, and the challenges of deploying models at scale
- Proficiency with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, Azure)
- Expertise in CI/CD principles and tools applied to machine learning workflows
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related quantitative field
What the job involves
- You’ll own the full lifecycle of model inference and hardware acceleration, from initial optimization to large-scale deployment
- In this role, you will be a key contributor to our team, ensuring our AI solutions are not just functional but also incredibly fast, efficient, and reliable on various inference hardware platforms
- You'll design, build, and maintain robust pipelines and runtime environments for deploying and serving machine learning models at the Edge. Ensure high availability, low latency, and efficient resource utilization for inference at scale
- Collaborate with researchers and hardware engineers to optimize models for performance, latency, and power consumption on specific hardware, including GPUs, TPUs, NPUs, and FPGAs. This includes a strong focus on inference optimization techniques like quantization, pruning, and knowledge distillation
- Use of AI compilers and specialized software stacks (e.g., TensorRT, OpenVINO, TVM) to accelerate model execution, ensuring models are compiled and optimized for peak performance on target hardware
- Design, build, and maintain MLOps pipelines for deploying models to various edge devices (e.g., highly integrated vehicle compute), with a specific focus on performance and efficiency constraints
- Implement and maintain monitoring and alerting systems to track model performance, data drift, and overall model health in production
- Work with cloud platforms and on-device environments to provision and manage the necessary infrastructure for scalable and reliable model serving
- Proactively identify and resolve issues related to model performance, deployment failures, and data discrepancies, with a specific focus on inference bottlenecks
- Work closely with Machine Learning Engineers, Software Engineers, and Product Managers to bring models from design to high-performance production systems
Our take
Software-defined vehicles represent the biggest innovation in automobiles for decades, but there are large obstacles in the way of their growth. The software that defines such vehicles requires over-the-air (OTA) updates and other forms of maintenance, which can quickly become a time-consuming and resource-intensive process.
Sonatus develops cloud-based solutions for software-defined vehicles, providing a platform called Digital Dynamics that empowers automakers to harness vehicle data, add feature updates, diagnose problems, and maintain vehicle infrastructure wirelessly and in real time. The company achieves its flexibility through cloud-empowered data infrastructure, no-code software solutions, and working closely with clients.
Whilst the company faces competition from other IoT vehicle software companies, Sonatus shows great potential to compete through its all-in-one approach. This has earned it impressive funding, which supports further development and the increase of production volume, as well as the company's expansion into the Japanese market.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (2 rounds)
Dec 2022
$75m
SERIES A
Jul 2021
$35m
SERIES A
Total funding: $110m
Company benefits
- Stock option program
- Flexible working arrangements
- Unlimited PTO
- Comprehensive benefits with generous allowance
- Free in-office food & snacks!
Company HQ
Sunnyvale, CA
Leadership
Jeffrey Chou
(Co-Founder & CEO)Experience as Senior Director of Engineering for Nuova Systems. Founder of Diamanti, where they were CEO and now serve as a Board Member.
Yu Fang
(Co-Founder & CTO/CPO)Head of Mobile Engineering for NAUTO from 2016 to 2018. Experience as CTO of CARDFREE.
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