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Embedded Machine Learning Engineer Jobs in Pittsburg, CA

S27a

Machine Learning Engineer

San Francisco, CA · On-site

$140 - $210/hr

Maintain, monitor, and enhance deployed machine learning systems to ensure continuous improvement. * Collaborate with software engineers, data scientists, and product teams to integrate AI solutions.

New

Jobzhr

Machine Learning Engineer

Pleasanton, CA · On-site

$110 - $150/hr

... with machine learning frameworks such as TensorFlow, Keras, and PyTorch. Knowledge of cloud platforms and technologies, specifically Microsoft Azure, is crucial. Experience in DevOps and MLOps ...

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$180 - $260/hr

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Canals

Machine Learning Engineer

San Francisco, CA · Remote

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$250K - $385K/yr

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

Relace

Machine Learning Engineer

San Francisco, CA · On-site

The Role We're looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable ...

Sift

Machine Learning Engineer

San Francisco, CA · On-site

$160 - $230/hr

As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won't just train models in isolation; you will build end-to-end pipelines ...

Clay Labs

Machine Learning Engineer

San Francisco, CA · On-site

$170K - $300K/yr

Machine Learning Engineer @ Clay Clay's ambition is to build a self-learning revenue engine : a product that gets smarter every time someone uses it. This means data, ML, and AI are at the heart of ...

New

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

What we're seeking A visionary Machine Learning Engineer to join our founding team who will help ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

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Showing results 21-40

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

Embedded Machine Learning Engineer information

See Pittsburg, CA salary details

$77.8K

$170.5K

$193.4K

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

As of Aug 16, 2026, the average yearly pay for embedded machine learning engineer in Pittsburg, CA is $170,491.00, according to ZipRecruiter salary data. Most workers in this role earn between $146,200.00 and $192,300.00 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 Pittsburg, CA?

For Embedded Machine Learning Engineer jobs in Pittsburg, CA, the most frequently searched job titles are:

What job categories do people searching Embedded Machine Learning Engineer jobs in Pittsburg, CA look for?

The top searched job categories for Embedded Machine Learning Engineer jobs in Pittsburg, CA are:

What cities near Pittsburg, CA are hiring for Embedded Machine Learning Engineer jobs?

Cities near Pittsburg, CA with the most Embedded Machine Learning Engineer job openings:

Embedded Machine Learning Engineer jobs near you
Infographic showing various Embedded Machine Learning Engineer job openings in Pittsburg, CA as of July 2026, with employment types broken down into 1% As Needed, 65% Full Time, 31% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $170,491 per year, or $82 per hour.

Machine Learning Engineer

S27a

San Francisco, CA • On-site

$140 - $210/hr

Other

Posted yesterday

New

Job description

Responsible for developing next-generation AI systems designed to simplify task automation for users. This role involves designing, evaluating, deploying, and maintaining AI solutions, utilizing both Large Language Models (LLMs) and Bardeen's custom models in areas such as semantic parsing, dialog systems, agents, and text generation. The position collaborates with engineers to integrate AI features into Bardeen's products, ensuring a high-quality user experience.

Specific duties include:

  • Research, design, and implement machine learning algorithms to optimize workflow automation.

  • Develop, test, and modify computer programs to apply machine learning models to real-world applications.

  • Research, design, and implement machine learning and AI algorithms to model real world processes, including process discovery, process conformance, and opportunity identification for automation and AI agents.

  • Develop, test, and modify computer programs that apply machine learning models to operational data sources such as event logs, clickstreams, tickets, documents, and call transcripts.

  • Design and improve methods for process and entity extraction from unstructured and semi structured data, including tasks, systems, stakeholders, and key business objects.

  • Stay familiar with and evaluate state of the art research in process mining, workflow intelligence, representation learning for events and processes, and LLM based planning and tool use, and translate it into practical enterprise solutions.

  • Perform statistical analysis and apply data mining techniques to diagnose bottlenecks, measure impact, and improve model performance and robustness in production settings.

  • Deploy machine learning models into production systems, ensuring scalability and efficiency.

  • Maintain, monitor, and enhance deployed machine learning systems to ensure continuous improvement.

  • Collaborate with software engineers, data scientists, and product teams to integrate AI solutions.

  • Prepare technical documentation and reports detailing methodologies and outcomes.

  • Utilize cloud computing platforms such as AWS and GCP to manage large-scale data processing and storage.

  • Ensure compliance with industry standards, data governance, and security protocols for machine learning applications.

Job Requirements:

Requires a Master's degree in Computational Science and Engineering, or a closely related field that focuses on Machine Learning, and 1 year of experience.

Experience must include:

  • Experience with modern deep learning models, particularly large language models (LLMs) and multimodal architectures used for understanding text, structured data, and behavioral traces.

  • Familiarity with OpenAI, Anthropic, or Hugging Face Transformers (GPT, Mistral, LLaMA, etc.).

  • Experience with Python, Hugging Face, and OpenAI, Gemini and Anthropic SDKs.

  • Experience with designing evaluation frameworks, benchmarking model variants, and measuring before/after impact.

  • Experience with production-grade data and inference infrastructure, including AWS and GCP.

  • Experience with monitoring, optimization, and scaling of LLM inference workloads across distributed systems.

  • Experience with ML and AI algorithms to model real world business processes and identification of high impact automation and AI agent opportunities.

  • Experience with using LLMs for performing statistical analysis.

Remote work is permitted. Travel is required to unanticipated locations nationwide. Travel is less than 5% of time.

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