Sr. AI / Embedded ML Engineer

• 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

• 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