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Embedded Machine Learning Engineer Jobs in Los Angeles, CA

Yum! Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data ...

Capgroup

Machine Learning Engineer II

Los Angeles, CA · On-site

$105K - $143K/yr

In this role you will work with a high performing team of applied scientists, machine learning engineers, and software development engineers that has delivered a number of AI/ML systems to production ...

Capgroup

Machine Learning Engineer II

Irvine, CA · On-site

$104K - $143K/yr

In this role you will work with a high performing team of applied scientists, machine learning engineers, and software development engineers that has delivered a number of AI/ML systems to production ...

Yum

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data ...

Turion Space

Machine Learning Engineer

Irvine, CA

As a Machine Learning Integration Engineer, you will help rapidly prototype, mature, and monitor ML/CV solution that are integral to Turion's Space Domain Awareness data products. You will work on ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

MS degree in computer science, engineering, or mathematics * 2-3 years of relevant experience in building deep learning solutions for computer vision problems * Proficient with at least one major ...

Bespoke Labs

Machine Learning Engineer

Northridge, CA · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

... rigorous engineering with learning systems proven in globally deployed solutions that deliver ... What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Los Angeles, CA

Embedded Machine Learning Engineer information

See Los Angeles, CA salary details

$75.4K

$165.3K

$187.5K

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

As of Jul 20, 2026, the average yearly pay for embedded machine learning engineer in Los Angeles, CA is $165,272.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,700.00 and $186,400.00 per year, depending on experience, location, and employer.

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

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Los Angeles, CA? For Embedded Machine Learning Engineer jobs in Los Angeles, CA, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Los Angeles, CA look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Los Angeles, CA are:
What cities near Los Angeles, CA are hiring for Embedded Machine Learning Engineer jobs? Cities near Los Angeles, CA with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you
Sr Machine Learning Engineer

Sr Machine Learning Engineer

Yum! Brands

Irvine, CA

$112K - $154K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 12 days ago

Yum! Brands rating

3.9

Company rating: 3.9 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Job description

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data Scientists, Data Engineers, and Analytics stakeholders to deploy, maintain, and improve production ML workflows across AWS. The ideal candidate combines strong software engineering and MLOps fundamentals with a passion for building reliable, scalable machine learning systems.

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. 
  • 3+ years of experience in Machine Learning Engineering, MLOps, Software Engineering, or related technical roles. 
  • Strong Python development skills, including experience with pandas, PyTorch, scikit-learn, boto3, and SQL. 
  • Experience working with AWS services such as SageMaker, Step Functions, Lambda, S3, IAM, and ECR. 
  • Experience developing or supporting orchestration workflows using Airflow, Glue, or similar technologies. 
  • Familiarity with cloud-based data platforms such as Snowflake, Redshift, or Athena. 
  • Experience with Docker, CI/CD pipelines, source control workflows, and software development best practices. 
  • Strong troubleshooting and debugging skills across distributed systems and machine learning workflows. 
  • Ability to collaborate effectively with technical and non-technical stakeholders. 

Preferred Qualifications

  • Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. 
  • Familiarity with MLflow, Hydra/OmegaConf, FastAPI, or similar ML platform tooling. 
  • Experience supporting deep learning workflows in production environments. 
  • Exposure to infrastructure-as-code tools such as Terraform, Terragrunt, or CloudFormation. 
  • Experience working with customer analytics, marketing measurement, or recommendation systems. 

Salary Range: 129,800 - 162,200 annually + bonus eligibility. This is the expected salary range for this position. Ultimately, in determining pay, we'll consider the successful candidate's location, experience, and other job-related factors.

Benefits: Employees (and their eligible family members) may enroll in the following types of insurance coverage: medical, dental, vision, legal, and accidental death and dismemberment, as well as FSA/HSA (depending on enrolled medical plan). Yum! also provides short-term disability, long-term disability, and life insurance. Employees may enroll in our 401(k) plan. Yum! provides 4 weeks of vacation, paid sick leave, 10 paid holidays, a floating day off and 2 paid days for volunteer time each calendar year. To learn more about working at Yum! -Click here. 

At Yum!, one of our core values is to Believe in ALL People. This means seeing the value in everyone and unlocking their full potential to be their best self. YUM! Brands, Inc. (including its subsidiaries Yum Restaurant Services Group, LLC ("YRSG") and Yum Connect, LLC ("Yum Digital and Technology")(collectively, "Yum") is proud to be an equal opportunity employer and is committed to equity, inclusion, and belonging for all dimensions of diversity.  We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other protected characteristic. Yum! is committed to working with and providing reasonable accommodation to applicants with disabilities or special needs.

US Job Seekers/Employees - Click here to view the "Know Your Rights" poster and supplement and the Pay Transparency Policy Statement.

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. 
  • 3+ years of experience in Machine Learning Engineering, MLOps, Software Engineering, or related technical roles. 
  • Strong Python development skills, including experience with pandas, PyTorch, scikit-learn, boto3, and SQL. 
  • Experience working with AWS services such as SageMaker, Step Functions, Lambda, S3, IAM, and ECR. 
  • Experience developing or supporting orchestration workflows using Airflow, Glue, or similar technologies. 
  • Familiarity with cloud-based data platforms such as Snowflake, Redshift, or Athena. 
  • Experience with Docker, CI/CD pipelines, source control workflows, and software development best practices. 
  • Strong troubleshooting and debugging skills across distributed systems and machine learning workflows. 
  • Ability to collaborate effectively with technical and non-technical stakeholders. 

Preferred Qualifications

  • Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. 
  • Familiarity with MLflow, Hydra/OmegaConf, FastAPI, or similar ML platform tooling. 
  • Experience supporting deep learning workflows in production environments. 
  • Exposure to infrastructure-as-code tools such as Terraform, Terragrunt, or CloudFormation. 
  • Experience working with customer analytics, marketing measurement, or recommendation systems. 
  • Support the deployment, monitoring, and ongoing maintenance of media measurement and customer modeling systems in partnership with Data Science and Engineering teams. 
  • Develop and maintain SageMaker processing and training jobs, model endpoints, and supporting infrastructure across development, testing, and production environments. 
  • Contribute to Step Functions, Lambda functions, and Airflow (MWAA) workflows that orchestrate model training, scoring, retraining, and analytics pipelines. 
  • Support MLflow model registration and promotion processes, configuration management, and versioned model artifacts. 
  • Build and maintain Docker images, ECR repositories, and GitLab CI/CD pipelines to enable reliable model deployment and release processes. 
  • Help productionize machine learning models and data pipelines that support customer analytics, scoring, and decisioning use cases. 
  • Investigate and resolve production issues using CloudWatch, DataDog, SageMaker logs, and workflow monitoring tools. 
  • Collaborate with cross-functional partners to implement platform enhancements, improve operational reliability, and deliver new capabilities. 
  • Contribute to engineering best practices, documentation, testing strategies, and operational procedures. 

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