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Embedded Tinyml Jobs (NOW HIRING)

E-Space

AI / Embedded ML Engineer

Saratoga, CA · Hybrid

$145K - $190.80K/yr

... 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 ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190.80K/yr

... 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 ...

Actalent

Sr. Staff Embedded AI Engineer

Columbia, MD · On-site

$122.10K - $160K/yr

Design, implement, and maintain TinyML and embedded AI solutions targeting microcontroller and MPU platforms such as RA, RL78, RX, and RZ. * Develop and enhance a cloud-based model translation ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190.80K/yr

... 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 ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · Hybrid

$145K - $190.80K/yr

... 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 ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190.80K/yr

... TinyML, embedded AI, and edge computing and bring relevant innovations into the team • Work closely with cross-functional teams including hardware engineers, firmware developers, and data ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190.80K/yr

... TinyML, embedded AI, and edge computing and bring relevant innovations into the team • Work closely with cross-functional teams including hardware engineers, firmware developers, and data ...

Renesas

Sr. Staff Embedded AI Engineer

Columbia, MD · On-site

$122.10K - $160K/yr

Company Description Renesas is seeking a Sr. Staff Embedded AI Engineer to develop advanced TinyML and embedded AI solutions targeting Renesas microcontroller and MPU platforms (RA, RL78, RX, RZ)

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How much do embedded tinyml jobs pay per year?

As of Jun 4, 2026, the average yearly pay for embedded tinyml in the United States is $153,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Embedded TinyML Engineer, and why are they important?

To thrive as an Embedded TinyML Engineer, you need a solid background in embedded systems, machine learning fundamentals, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with microcontroller platforms (such as ARM Cortex-M), TinyML frameworks (like TensorFlow Lite for Microcontrollers), and hardware debugging tools is typically required. Strong problem-solving skills, attention to detail, and the ability to communicate technical concepts clearly set standout professionals apart in this field. These skills are crucial for efficiently developing, deploying, and maintaining machine learning models on resource-constrained devices, ensuring reliable and innovative edge AI solutions.

What are some common challenges faced when deploying TinyML models on embedded devices?

Deploying TinyML models on embedded devices often involves managing strict resource constraints, such as limited memory, processing power, and energy consumption. Developers must carefully optimize model size and inference speed while ensuring accuracy remains acceptable for the application. Additionally, integrating models within existing embedded system workflows and testing for reliability under various real-world conditions can be challenging. Collaborating with hardware engineers and firmware developers is crucial to achieve efficient deployment and seamless operation.

What are Embedded TinyML engineers?

Embedded TinyML engineers specialize in developing and deploying machine learning models that run directly on small, resource-constrained embedded devices, such as microcontrollers and IoT sensors. Their work involves optimizing neural networks to ensure efficient performance with limited memory, power, and computational capacity. These engineers bridge the gap between embedded systems and AI, enabling smart features in everyday devices without the need for cloud connectivity.
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AI / Embedded ML Engineer

E-Space

Saratoga, CA • Hybrid

$145K - $190.80K/yr

Full-time

Medical, PTO

Posted 19 days ago

Job description

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.

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

What you bring to this role:

  • 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 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

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

$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.
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