AI​/Embedded ML Engineer

Ready to make connectivity from space universally accessible, secure and actionable? Then you’ve 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.

We’re intentional, we’re unapologetically curious and we’re 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 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.

• 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 Tensor Flow Lite Micro, Edge Impulse, ONNX Runtime, and Execu Torch.
  • 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.

• 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…