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Embedded Machine Learning Engineer Jobs in Sacramento, CA

Bespoke Labs

Machine Learning Engineer

Sacramento, 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 ...

Intel

Sr. Machine Learning Engineer

Folsom, CA

$111K - $153K/yr

Machine Learning Engineer / Data Scientist** to join our team, working on agent harness research and model fine tuning. This role sits at the intersection of research and engineering: the ideal ...

Deloitte

Senior AI Engineer - SFL Scientific

Sacramento, CA · On-site

$111K - $153K/yr

Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...

Pwc

AI Engineer

Sacramento, CA · On-site

$55K - $187K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials ...

Deloitte

Cyber - SecOps - Consultant

Sacramento, CA

... engineering, and the use of artificial intelligence and machine learning for cyber defense and ... embedded and as-a-service delivery models, and strengthen day-to-day security operations. The team ...

Pwc

AI Solutions Engineering Delivery Lead

Sacramento, CA · On-site

$109K - $144K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Deloitte

Lead Generative AI Data Engineer III

Sacramento, CA · On-site

$109K - $144K/yr

Lead the design, development, testing, and deployment of machine learning and artificial intelligence solutions for business and client use cases. * Manage AI engineering workstreams by assigning ...

Pwc

CTIO AI Engineering Manager

Sacramento, CA

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Deloitte

Lead Forward Deployed Engineer - AWS

Sacramento, CA · On-site

$109K - $144K/yr

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Sacramento, CA

Embedded Machine Learning Engineer information

See Sacramento, CA salary details

$74.6K

$163.6K

$185.5K

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

As of Aug 9, 2026, the average yearly pay for embedded machine learning engineer in Sacramento, CA is $163,556.00, according to ZipRecruiter salary data. Most workers in this role earn between $140,200.00 and $184,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an embedded machine learning engineer?

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 Sacramento, CA? For Embedded Machine Learning Engineer jobs in Sacramento, CA, the most frequently searched job titles are:
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Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Sacramento, CA

Full-time

Re-posted 22 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

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 preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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