AI / Embedded ML Engineer

About the role

AI / Embedded ML Engineer

Position Overview

E-Space is constructing a next-generation low Earth orbit network designed to make connectivity universally accessible, secure, and actionable. We bridge terrestrial and space-based systems to enable hyper-scaled Internet of Things deployments. The company is committed to innovating space-based communications, expanding global economies, protecting the planet, and enhancing quality of life. We are currently seeking an AI / Embedded ML Engineer to join this mission. This role reports to the Head of Product Engineering and is based in Saratoga, California.

This position is central to translating raw data from space into intelligence at the edge. You will own the full lifecycle of machine learning on resource-constrained hardware. The work spans data ingestion from orbital and terrestrial sources, model development, optimization, and deployment on embedded devices. You will build reliable, low-power, real-time ML systems that function in orbit and on the ground. Success requires expertise in sensor data processing, lightweight model design, embedded software, and hybrid LLM integration to deliver production-ready solutions. You will work closely with hardware, firmware, software, and data teams.

Key Responsibilities

You will design data ingestion paths that normalize spacecraft telemetry and ground sensor inputs while adhering to strict latency requirements. The role involves constructing lightweight neural structures that fit within tight memory constraints without sacrificing predictive accuracy. You will refactor models for fixed-point execution, ensuring stability across extreme temperature variations and radiation effects.

Coordination with hardware groups is essential to align compute schedules, memory maps, and power envelopes. You will verify system behavior through simulation, replayed logs, and on-orbit tests before any feature activation. Streamlining pipelines is a core duty, ensuring models integrate cleanly with firmware, middleware, and control software. You will negotiate tradeoffs between algorithmic complexity and real-time response for mission-critical tasks.

Collaboration with data and firmware teams is required to ship updates that improve insights without disrupting operations. The role demands comfort with sensor streams, model formats, and embedded code. You will write tests and documentation that maintain system clarity throughout long-duration missions. Clear communication with engineers, scientists, and operators across multiple time zones is mandatory.

Professional Requirements

The candidate must handle sensor data, model formats, and embedded code with equal proficiency. A minimum of 3-5 years of experience shipping machine learning on resource-limited hardware in demanding environments is required. You must possess a deep understanding of quantization, compression, and optimization methods for low-power processors.

The ability to write tests and documentation that ensure system understandability over long missions is essential. Clear communication skills are necessary to interface with cross-functional teams globally.

Nice to Have

Experience with satellite communication protocols, ground stations, and mission operations is valued but not required.

Tools and Skills

Proficiency in C and Python is required. Experience with TensorFlow Lite, embedded Linux, Git, CI, and simulation tools is expected.

Practical Information

Please What you'll do

• Execute the core responsibilities of the AI / Embedded ML Engineer position at Espace as outlined in the official description.
• Translate mission objectives into embedded machine learning workflows that operate under severe resource and latency constraints.
• Architect data ingestion layers to manage heterogeneous inputs from space-based telemetry and terrestrial sensor networks.
• Engineer compact neural architectures that maintain high accuracy while operating within severe memory and compute limitations.
• Refactor existing models to support fixed-point arithmetic, ensuring robustness against the harsh environmental conditions of space.
• Synchronize closely with hardware engineers to align processing schedules, memory layouts, and power consumption targets.
• Validate system integrity through rigorous simulation, log replay, and in-orbit testing procedures prior to deployment.
• Streamline integration points so models operate seamlessly with firmware stacks, middleware frameworks, and control logic.
• Balance algorithmic sophistication against real-time performance demands for safety and mission-critical applications.
• Partner with data science and firmware teams to release incremental improvements that enhance insight quality without operational disruption.
• Contribute to the reliability of space systems by authoring comprehensive tests and maintaining clear, durable documentation.
• Communicate effectively with global stakeholders, including engineers, scientists, and operations personnel across distributed time zones.

Requirements

• Demonstrate advanced competence in both sensor data handling and embedded codebases, moving fluidly between data formats and low-level implementation.
• Bring a minimum of 3-5 years of professional experience deploying machine learning on resource-constrained hardware in challenging operational environments.
• Exhibit deep knowledge of quantization strategies, model compression, and optimization tactics tailored to low-power processors.
• Produce tests and documentation that preserve system clarity and maintainability throughout extended mission durations.
• Interface effectively with cross-functional teams distributed across international locations, maintaining clarity and alignment.
• Commit to the rigorous standards required for space-based systems where failure modes have significant consequences.
• Apply disciplined engineering practices to ensure models function correctly under thermal, radiation, and power constraints.
• Maintain a mindset focused on reliability, safety, and performance when implementing machine learning in embedded contexts.

Nice to have

• Familiarity with satellite communication protocols, ground station operations, and broader mission control procedures.

Practical notes

This listing specifies the location as Saratoga, California. The application process must be completed through the designated apply page to confirm current duties, compensation, and location specifics. Prospective candidates are urged to submit applications via the official channel to ensure consideration for this role.