Senior 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.

We are hiring a Sr. AI / Embedded ML Engineer to own the full lifecycle of machine learning on resource-constrained hardware. You will take models from data ingestion through development, optimization, and deployment on embedded devices. You will build reliable, low-power, and real-time ML systems that run on the edge.

This is a hands-on role for someone who combines sensor data processing, lightweight model design, embedded software, and hybrid LLM integration. You will deliver production-ready ML solutions on real hardware, and you will work closely with hardware, firmware, software, and data teams.

Your mission Build edge ML systems that are accurate, fast, and efficient on small devices. Improve model accuracy, inference latency, memory footprint, and power use with every release. Turn sensor data into dependable product features. Move quickly from prototype to field-tested deployment and make decisions with limited data. Build a culture that ships working ML on hardware and grow a team that delivers on time.

• Data ingestion and pipelines. Design and build data ingestion pipelines from IMUs, accelerometers, gyroscopes, microphones, and other environmental sensors. Clean, label, synchronize, and store raw sensor data. Build tools to collect, version, and manage training datasets at scale.
• Model development and training. Develop and train models for classification, regression, anomaly detection, and signal processing. Select the right architecture for each problem and hardware target. Fine-tune pre-trained models for domain-specific data, and run experiments to compare performance.
• TinyML and embedded deployment. Optimize models for ARM Cortex-M, RISC-V, and DSP targets using quantization, pruning, and knowledge distillation. Deploy with TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, or ExecuTorch. Integrate inference into C, C++, or Rust firmware, and profile memory, power, and real-time performance.
• Hybrid LLM integration. Design hybrid architectures that combine on-device models with LLM-based reasoning. Build pipelines that route tasks between edge inference and cloud or edge-hosted LLMs. Evaluate tradeoffs in latency, accuracy, and power.
• Embedded software and systems integration. Write clean, well-tested embedded software that puts ML inference into real-time systems. Work with FreeRTOS, Zephyr, and bare-metal firmware. Co-optimize the full system stack with hardware and firmware teams.
• Documentation and reporting. Document design decisions, pipeline configurations, model benchmarks, and deployment procedures. Prepare technical reports and presentations for internal teams and stakeholders. Bring relevant TinyML and edge AI advances into the team.
• Bring-up and field support. Provide technical support during hardware bring-up, system integration, and field testing. Take part in design reviews and give useful feedback across the stack.
• Lasting corrective action. Contain field and model issues fast. Find root causes, such as data drift or sensor noise, and verify that fixes prevent recurrence.
• Measurable results. Own clear, tracked targets for each model and device. Meet accuracy targets on real sensor data. Keep inference latency, memory footprint, and power per inference within budget. Ship models that pass field testing on the first attempt where possible. Keep the training data versioned and reproducible.

• Strong background in signal processing, sensor data handling, and real-time system constraints.
• Hands-on experience with IMUs and other sensors, including accelerometers, gyroscopes, barometers, and microphones.
• Proficiency in Python for ML development with PyTorch, TensorFlow, or scikit-learn.
• Experience with C or C++ for embedded systems development.
• Solid understanding of model optimization, 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, with the ability to work independently and as part of a team.
• A degree in electrical engineering, computer engineering, computer science, or a related field, or equivalent practical experience.

A plus but not required Experience with FreeRTOS or Zephyr, familiarity with MCU families such as NXP, STM32, or ESP32, experience designing hybrid edge-LLM pipelines or running small language models on device, background in feature extraction (FFT, filter banks, wavelet transforms), experience with hardware-aware neural architecture search or AutoML for edge targets, familiarity with Rust for embedded systems, and prior work on wearables, robotics, industrial sensing, or IoT products.

What success looks like Models run accurately on real devices within their memory and power budgets. Sensor data flows reliably from device to dataset. Hybrid ML systems route work well between the edge and the cloud. Field issues surface early and get fixed quickly. Your teammates grow, and the team ships edge ML that works in the field.

This is a full time, exempt position, based out of our Santa Clara, CA location. The target base pay for this position in Santa Clara, California is $150,000 - $225,000 annually. The total compensation package will be determined by various factors such as your relevant job-related knowledge, skills, and experience and location.

We are redefining how satellites are designed, manufactured and used—so we’re looking for candidates with passion, deep knowledge and direct experience on LEO satellite component development, design and in-orbit activities. If that’s your experience – then we’ll be immediately wow-ed.

E-Space is not currently able to provide employment sponsorship for candidates who do not hold work authorization for the location of this role.

Why E-Space is right for you

As a member of our team, you will play a crucial role in driving our success. Our team members have a strong sense of dedication and responsibility; this includes a strong commitment to our mission to create an entirely new suite of global capabilities to improve lives, business efficiencies and build a smarter planet. This means that there will be times when extra hours, including nights and weekends, may be needed to meet critical deadlines and mission goals.