Sr. 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 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.

What you will do:

• 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
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What you bring to this role:

• 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
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Bonus points for the following:

• 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
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$150,000 - $225,000 a year

This is a full time, exempt position, based out of our Saratoga office. The total compensation packaged will be determined by various factors such as your relevant job-related knowledge, skills, and experience.

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. In return, we offer a dynamic work environment with opportunities for professional growth and development and the chance to make a meaningful impact in a high-growth industry.

We want you to make the most of your journey at E-Space. That's why we support and invest in the physical, emotional and financial well-being of our team members and their families. Some of what you can expect when working at E-Space:

An opportunity to really make a difference

Sustainability at our core

Fair and honest workplace

Innovative thinking is encouraged

Competitive salaries

Continuous learning and development

Health and wellness care options

Financial solutions for the future

Optional legal services (US only)

Paid holidays

Paid time off

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.