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Senior Embedded Machine Learning Jobs (NOW HIRING)

Motorola

Machine Learning Engineer

Los Angeles, CA · On-site

Familiarity with embedded machine learning, real-time systems, or deploying machine learning on ... edge devices. * Background in adaptive modulation, beamforming, or cognitive radio techniques.

Symbotic

Senior Embedded Software Engineer

Austin, TX · On-site

$122K - $161K/yr

As a Senior Embedded Software Engineer, you will play a key role in designing, developing, and ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

Symbotic

Senior Embedded Software Engineer

Austin, TX · Hybrid

$122K - $161K/yr

As a Senior Embedded Software Engineer, you will play a key role in designing, developing, and ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

PROPRIUS

Senior Machine Learning Engineer

New York, NY · On-site

$114K - $157K/yr

Sr. Machine Learning Engineer Location: New York, NY Sponsorship: Yes Relocation: Yes Industry ... with machine learning in embedded applications: model quantization, fixed point neural networks ...

Peoplentech LLC

Sr. Embedded SW Engineer

Palo Alto, CA

$145K - $191K/yr

Title: Sr. Embedded SW Engineer Location: Palo Alto, CA Duration: 2 Years Save Lives - Develop ... machines. Here are some highlights of this position: Design and develop Object Oriented real time ...

Peoplentech LLC

Sr. Embedded SW Engineer

Palo Alto, CA · On-site

$145K - $191K/yr

Title: Sr. Embedded SW Engineer Location: Palo Alto, CA Duration: 2 Years Save Lives - Develop ... machines. Here are some highlights of this position: Design and develop Object Oriented real time ...

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Senior Embedded Machine Learning information

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$75.5K

$144.8K

$193.5K

How much do senior embedded machine learning jobs pay per year?

As of Jul 11, 2026, the average yearly pay for senior embedded machine learning in the United States is $144,773.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,000.00 and $162,500.00 per year, depending on experience, location, and employer.

What is the difference between Senior Embedded Machine Learning vs Embedded Software Engineer?

AspectSenior Embedded Machine LearningEmbedded Software Engineer
Required CredentialsBachelor's/Master's in CS, EE, or related; experience in ML and embedded systemsBachelor's in CS, EE, or related; strong programming skills in C/C++
Work EnvironmentDeveloping ML models for embedded devices, hardware integrationDesigning and implementing embedded software for devices
Industry UsageAI/ML-focused companies, IoT, consumer electronicsAutomotive, industrial, consumer electronics

While both roles involve embedded systems, Senior Embedded Machine Learning focuses on integrating ML models into hardware, requiring knowledge of AI and data science. Embedded Software Engineers primarily develop software for embedded devices, emphasizing firmware and system-level programming. The roles overlap in embedded environment skills but differ in their core focus on AI versus traditional software development.

What are some common challenges faced by Senior Embedded Machine Learning Engineers when deploying models on edge devices?

Senior Embedded Machine Learning Engineers often encounter challenges such as optimizing model size and inference speed to fit within the limited computational resources and memory of edge devices. Balancing accuracy and performance while minimizing power consumption is critical, especially for battery-operated products. Additionally, integrating models with existing embedded software and ensuring reliable, real-time operation can require close collaboration with hardware and firmware teams. Staying current with advancements in model compression and hardware acceleration is also essential for success in this role.

What are the key skills and qualifications needed to thrive as a Senior Embedded Machine Learning Engineer, and why are they important?

To thrive as a Senior Embedded Machine Learning Engineer, you need expertise in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often backed by an advanced degree in computer science or electrical engineering. Familiarity with tools such as TensorFlow Lite, ONNX, and embedded hardware platforms (e.g., ARM Cortex-M, NVIDIA Jetson) is typically required. Strong problem-solving, project management, and communication skills distinguish top performers in this role. These capabilities are crucial for efficiently deploying optimized machine learning models on resource-constrained devices and effectively collaborating across multidisciplinary teams.

What does a Senior Embedded Machine Learning engineer do?

A Senior Embedded Machine Learning engineer designs, develops, and optimizes machine learning models to run efficiently on resource-constrained embedded devices such as microcontrollers, IoT devices, and edge hardware. They are responsible for integrating ML algorithms with embedded systems, ensuring low latency and minimal power consumption. Their work often involves collaborating with hardware engineers and software developers to deploy intelligent features in products like smart sensors, wearables, and autonomous systems.
What cities are hiring for Senior Embedded Machine Learning jobs? Cities with the most Senior Embedded Machine Learning job openings:
What are the most commonly searched types of Embedded Machine Learning jobs? The most popular types of Embedded Machine Learning jobs are:
What states have the most Senior Embedded Machine Learning jobs? States with the most job openings for Senior Embedded Machine Learning jobs include:
Senior Embedded Machine Learning jobs near you

Sr. Embedded Machine Learning Engineer

Allen Control Systems

Austin, TX • On-site

$122K - $161K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 2 days ago

New

Job description

Senior Embedded Machine Learning Engineer for Autonomous Anti-Drone Systems
Company Overview:
Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.
With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders' successful exits from two previous venture acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact.
Position Summary:
We are hiring a Senior Embedded Machine Learning Engineer to own the end-to-end process of taking trained machine learning models including any code supporting them and deploying them efficiently onto resource-constrained edge hardware. This person sits at the intersection of machine learning, embedded systems, and hardware engineering.
The role has two tightly linked primary responsibilities: integrating, converting, and optimizing models so they run within strict constraints on latency, memory, power, and thermal budget; and building and integrating the supporting C++ code that runs the models on device and performs any necessary pre or post processing. The role is highly cross-functional. You will partner with CVML who build the models, with embedded and firmware teams who own the device, and with product teams who define performance targets. Success means models that are not just accurate in the lab but fast, small, and dependable in the field.
What You'll Do:
• Model optimization. Apply quantization, pruning, knowledge distillation, operator fusion, and graph optimization to shrink models and reduce inference cost while protecting accuracy.
• Model conversion and deployment. Convert trained models into formats suitable for edge runtimes using ONNX and TensorRT and deploy them to target hardware.
• Hardware bring-up and benchmarking. Profile inference on accelerators such as GPUs, NPUs, DSPs, TPUs, or FPGAs. Measure latency, throughput, memory footprint, and power, then drive the changes needed to hit targets.
• C++ application integration. Design, write, and maintain the supporting C++ code that hosts inference on device. This includes the application and library code that loads and runs models, the pre- and post-processing pipelines, data and memory management, threading, and the interfaces to the rest of the embedded system. Ensure the combined model and C++ stack meets real-time constraints, fits within the device memory budget, and behaves reliably on the target platform, using Python where appropriate for tooling and validation.
• Accuracy and quality validation. Build test harnesses that verify on-device accuracy against reference results and catch regressions introduced by optimization or quantization.
• Model update pipeline. Contribute to the tooling and processes for packaging, versioning, and delivering model updates to deployed devices, including over-the-air update paths where applicable.
• Cross-functional collaboration. Work closely with research, firmware, and product teams to set realistic performance targets early and to feed hardware constraints back into model design.
• Technical leadership. Set best practices for edge deployment, review designs and code, and mentor other engineers on optimization and embedded ML techniques.
What You'll Do:
  • Development and optimization of computer vision algorithms for our autonomous gun turret, focusing on real-time drone detection, tracking, and classification.
  • Design and implement machine learning models that can operate in resource-constrained environments while maintaining high accuracy and reliability.
  • Collaborate closely with electrical engineers to integrate computer vision systems into the turret's hardware architecture.
  • Conduct extensive testing and validation of computer vision algorithms in various scenarios to ensure robustness and performance under different environmental conditions.
  • Contribute to the hardening of the prototype turret into a military-grade system, and assist in developing variants for different weapon systems and engagement ranges.

What You'll Need:
  • A Bachelor's or Master's Degree in Computer Science, Electrical Engineering, Computer Engineering or a related field, or equivalent practical experience.
  • 10+ or more years of professional software or systems engineering experience, including at least 2 years focused on deploying ML models to embedded or edge devices.
  • Very strong proficiency in C/C++ (or Python but C++ most important)
  • Proficiency with CUDA
  • Hands-on experience with PyTorch and with at least one edge runtime or inference format (TensorFlow Lite, ONNX Runtime, TensorRT, or similar).
  • Practical experience with model optimization techniques such as quantization (post-training and quantization-aware), pruning, or distillation.
  • Demonstrated ability to profile and optimize for latency, memory, and power on constrained hardware.
  • Working knowledge of embedded or edge platforms (for example NVIDIA Jetson, Google Coral, Qualcomm, ARM Cortex, or comparable NPUs and SoCs) and of Linux or an RTOS.
  • Solid grasp of computer architecture concepts relevant to inference, including memory hierarchy, fixed-point arithmetic, and accelerator offload.
  • Domain experience in computer vision or sensor processing on device

What We Offer:
  • Competitive salary
  • ACS Equity Package
  • Health, Dental, Vision Insurance
  • Paid Time Off

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

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