AI / Embedded ML Engineer

As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/ machine learning on resource-constrained hardware. This includes data ingestion, model development, optimization, and deployment on embedded devices. This role is critical for building reliable, low-power, real-time ML systems that operate at the edge.

In this role, you will leverage your expertise in sensor data processing, lightweight model design, embedded software, and hybrid LLM integration to deliver production-ready ML solutions on hardware.

This position will report to Head of Product Engineering, and you will work closely with hardware, firmware, software, and data teams. This position is based in Saratoga, CA.

What you will do:

• • Data Ingestion and Pipeline Development

  ◦ Design and build data ingestion pipelines from sensors including IMUs, accelerometers, gyroscopes, microphones, and other environmental sensors

  ◦ Handle raw sensor data: cleaning, labeling, synchronization, and storage

  ◦ Build tools to collect, version, and manage training datasets at scale

  • Model Development and Training

  ◦ Develop and train ML models for classification, regression, anomaly detection, and signal processing tasks

  ◦ Select appropriate model architectures for each problem and hardware target

  ◦ Fine-tune pre-trained models for domain-specific tasks and data distributions

  ◦ Design and run experiments to evaluate and compare model performance

  • TinyML and Embedded Deployment

  ◦ Optimize models for deployment on microcontrollers and edge processors such as ARM Cortex-M, RISC-V, and DSPs

  ◦ Apply quantization, pruning, and knowledge distillation to reduce model size and inference latency

  ◦ Use frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch

  ◦ Integrate ML inference into embedded firmware written in C, C++, or Rust

  ◦ Profile and optimize memory usage, power consumption, and real-time performance

  • Hybrid LLM Integration

  ◦ Design hybrid architectures that combine on-device lightweight models with LLM-based reasoning

  ◦ Build pipelines that route tasks between edge inference and cloud or edge-hosted LLM components

  ◦ Evaluate trade-offs in latency, accuracy, and power between on-device and LLM-assisted approaches

  • Software Embedding and Systems Integration

  ◦ Write clean, well-tested embedded software that integrates ML inference into real-time systems

  ◦ Work with RTOS environments such as FreeRTOS and Zephyr, as well as bare-metal firmware

  ◦ Collaborate with hardware and firmware teams to co-optimize the full system stack

  • Documentation and Reporting

  ◦ Document design decisions, pipeline configurations, model benchmarks, and deployment procedures

  ◦ Prepare technical reports and presentations for internal teams and stakeholders

  ◦ Stay current with developments in TinyML, embedded AI, and edge computing and bring relevant innovations into the team

  • Collaboration and Support

  ◦ Work closely with cross-functional teams including hardware engineers, firmware developers, and data scientists

  ◦ Provide technical support during hardware bring-up, system integration, and field testing

  ◦ Participate in design reviews and contribute constructive feedback across the stack

What you bring to this role:

• • 2+ years of experience in machine learning engineering, with at least 2 years focused on embedded or edge ML

  • Strong background in signal processing, sensor data handling, and real-time system constraints

  • Hands-on experience with IMUs and other sensor types including accelerometers, gyroscopes, barometers, and microphones

  • Proficiency in Python for ML development using frameworks such as PyTorch, TensorFlow, or scikit-learn

  • Experience with C or C++ for embedded systems development

  • Solid understanding of model optimization techniques including quantization, pruning, and distillation

  • Experience deploying models with at least one embedded ML framework such as TFLite Micro, Edge Impulse, or ONNX Runtime

  • Strong understanding of memory-constrained and power-constrained environments

  • Excellent problem-solving skills and the ability to work independently and as part of a team

Bonus points for the following:

• • Experience with RTOS platforms such as FreeRTOS or Zephyr

  • Familiarity with MCU families including NXP, STM32, ESP32, or similar

  • Experience designing hybrid edge-LLM pipelines or integrating small language models on device

  • Background in feature extraction techniques such as FFT, filter banks, and wavelet transforms

  • Experience with hardware-aware neural architecture search or AutoML for edge targets

  • Familiarity with Rust for embedded or systems programming

  • Prior work on products in wearables, robotics, industrial sensing, or IoT