8 days ago
Sunnyvale, CA, USAStaff+
Base Salary
$198k - $272k/yr
Responsibilities
- Build and train Edge AI models, including Transformers, LLMs, CNNs, LSTMs, and tree-based models, for logs, traces, and multimodal vehicle data.
- Integrate ML workflows and cloud-based LLM APIs, including Gemini, OpenAI, and Claude, with an emphasis on synthetic data generation.
- Develop clustering and anomaly-detection algorithms to identify log patterns, software regressions, race conditions, crash precursors, and vehicle health issues.
- Design supervised and unsupervised models for time-series data from CAN bus systems and onboard sensors.
- Correlate anomalies across vehicle signals, sensor data, and system events to identify root causes.
- Port and optimize PyTorch and TensorFlow models for production execution on CPU, GPU, and embedded NPU targets.
- Apply quantization, pruning, distillation, and memory optimization to meet RAM and Flash constraints.
- Define on-device data filtering and determine which data is processed locally versus in the cloud.
- Lead Edge ML pipeline architecture and mentor junior engineers on embedded AI best practices.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, Software Engineering, or a related field.
- At least 7 years of Machine Learning Engineering experience, including at least 3 years focused on Edge AI or embedded systems.
- Proven experience mentoring junior engineers and leading technical projects from concept through production.
- Expert Python and working knowledge of modern C++, including C++14/17 for inference.
- Deep proficiency with PyTorch or TensorFlow and experience with ONNX, TFLite, or TVM inference engines.
- Experience with NLP, sequence modeling, vector stores, lightweight LLMs or SLMs, and anomaly-detection libraries such as scikit-learn, tslearn, or statsmodels.
- Experience deploying to ARM-based Edge environments, manually managing memory, and working with limited compute resources.
- Strong communication skills and the ability to explain technical trade-offs to stakeholders.
- A strong Computer Vision or ADAS background is encouraged.
- A master’s or PhD in Computer Science, Engineering, or a related field is desired.
- Familiarity with automotive formats and protocols including CAN, DBC, UDS, SOME/IP, or MQTT is desired.
- Understanding of Linux and QNX kernel logs, dmesg, process states, and OS-level debugging is desired.
- Experience with NVIDIA TensorRT or Qualcomm SNPE is desired.
Benefits
- Medical, dental, and vision coverage.
- Flexible and Dependent Care Expense program.
- 401(k) retirement plan.
- Basic, voluntary, and AD&D life insurance.
- Unlimited paid time off and 14 or more paid holidays.
- Hybrid work arrangement in Sunnyvale, California, with three days per week in the office.
- Complimentary lunches, snacks, and beverages on onsite working days.
- Wellness benefit allowance.
- Phone and internet reimbursement.
- Computer accessory allowance.
