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Embedded Machine Learning Engineer Jobs in Pittsburg, CA

Atoms

Staff Machine Learning Engineer

San Francisco, CA · On-site

AI Researcher (World Models & VLA) What you'll do A visionary Machine Learning Engineer to join our ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Atoms

Staff Machine Learning Engineer

San Francisco, CA · On-site

AI Researcher (World Models & VLA) What you'll do A visionary Machine Learning Engineer to join our ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

AI Researcher (World Models & VLA) What you'll do A visionary Machine Learning Engineer to join our ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Jobzhr

Machine Learning Engineer

San Francisco, CA · On-site

$130 - $200/hr

About the Role We are seeking a Machine Learning Engineer to design, build, and evaluate advanced machine learning systems across AI safety and model evaluation applications. This role combines ...

Atoms

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

AI Researcher (World Models & VLA) What you'll do A visionary Machine Learning Engineer to join our ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Capable

Machine Learning Engineer

San Francisco, CA · On-site

$150K - $240K/yr

About the Role You will build machine-learning systems that remove real bottlenecks from drug discovery and development. The role spans research and engineering: identifying valuable problems ...

Conduit

Machine Learning Engineer

San Francisco, CA · On-site

Conduit builds autonomous factories through a single data abstraction layer across every machine ... Translate customer needs into clean engineering briefs for the SF team. Build relationships with ...

Firecrawl

Machine Learning Engineer

San Francisco, CA · On-site

$210K - $240K/yr

Machine Learning Engineer You'll build the ML behind Firecrawl - the models and the systems that serve them. That starts with search: training and shipping the ranking and relevance models for one of ...

Workday

Machine Learning Engineer III

Pleasanton, CA

About the Role As a Machine Learning Engineer on the AI Platform team, you will develop tailored user experiences using advanced Agentic AI, LLMs and RAG. You will collaborate with other engineers to ...

Kikoff

Senior Machine Learning Engineer

San Francisco, CA

$123K - $169K/yr

We are seeking a Senior Machine Learning Engineer to join our team. This role will focus on developing and maintaining machine learning infrastructure and operations, particularly for our cash ...

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Showing results 41-60

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 Sep 7, 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 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 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 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 August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $170,491 per year, or $82 per hour.

Machine Learning Engineer

Sift Science, Inc

San Francisco, CA • On-site, Remote

$140K - $190K/yr

Full-time

Re-posted 28 days ago

Key responsibilities

  • Build and deploy online machine learning models to detect evolving fraud vectors in real time.

  • Engineer high-frequency time-series features from behavioral events to optimize low-latency signal extraction.

  • Maintain and improve automated model training and deployment infrastructure to support continuous integration and deployment.

Job description

The Role:
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 that extract signals, train custom models per merchant, and serve predictions at production scale with low latency. You will work on an automated machine learning ecosystem that dynamically recalibrates models based on streaming global telemetry data.
What You'll Do:
  • Model Development & Refinement: Design, build, and deploy online machine learning models (including ensemble methods, deep learning, transformer architectures and graph-based models) to catch evolving fraud vectors in real time.
  • Feature Engineering at Scale: Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
  • Production MLOps: Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models.
  • System Optimization: Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
  • Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.
What We Are Looking For (Requirements):
  • Experience: 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments.
  • Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping).
  • Distributed Systems & Big Data: Practical experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
  • Strong Mathematical Foundations: Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
  • System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).
Bonus Points (Preferred Qualifications):
  • Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains.
  • Deep knowledge of streaming architectures (e.g., Apache Kafka).
  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Familiarity with leveraging AI coding assistants (e.g., Claude Code) to accelerate development and model prototyping

Please note: final stage candidates may be asked to travel for in-person final round interviews.
Let's build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community - ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.

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