Sr. AI / Embedded ML Engineer

E-Space•Saratoga, CA

1d•Onsite

About The Position

Ready to make connectivity from space universally accessible, secure and actionable? Then youâve come to the right place! E-Space is bridging Earth and space to enable hyper-scaled deployments of Internet of Things (IoT) solutions and services. We are building a highly-advanced low Earth orbit (LEO) space system that will fundamentally change the design, economics, manufacturing and service delivery associated with traditional satellite and terrestrial IoT systems. Weâre intentional, weâre unapologetically curious and weâre 100% committed to innovate space-based communications and deliver actionable intelligence that will expand global economies, protect space and our planet and enhance our overall quality of life. As a Senior 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.

Requirements

5+ 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

Nice To Haves

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

Responsibilities

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