E
E-Space
AI / Embedded ML Engineer
Skills
RustPythonPyTorchTensorFlowMachine LearningAIESP32
2 months ago
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Important information
Contract type:
Permanent
Salary:
$150,000 – $225,000 / year
Location:
Saratoga, CA, USA
Work mode:
Onsite
Published on:
4 September 2026
What they need
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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- Strong background in signal processing, sensor data handling, and real-time system constraints
- Solid understanding of model optimization techniques including quantization, pruning, and distillation
- Strong understanding of memory-constrained and power-constrained environments
- Proficiency in Python for ML development using frameworks such as PyTorch, TensorFlow, or scikit-learn
- Hands-on experience with IMUs and other sensor types including accelerometers, gyroscopes, barometers, and microphones
- Experience deploying models with at least one embedded ML framework such as TFLite Micro, Edge Impulse, or ONNX Runtime
- Experience with C or C++ for embedded systems development
- 2+ years of experience in machine learning engineering, with at least 2 years focused on embedded or edge ML
- 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
- Experience with hardware-aware neural architecture search or AutoML for edge targets
- Background in feature extraction techniques such as FFT, filter banks, and wavelet transforms
- Familiarity with Rust for embedded or systems programming
- Prior work on products in wearables, robotics, industrial sensing, or IoT
- Excellent problem-solving skills and the ability to work independently and as part of a team
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