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Embedded Machine Learning Engineer Jobs in Pasadena, CA

Socket.dev

Machine Learning Engineer

Los Angeles, CA · On-site

$180 - $250/hr

Role Description Founding Data Scientist / Machine Learning Engineer We're looking for a highly ambitious Data Scientist to help build the predictive intelligence layer behind nowfluence. This is not ...

Hadrian Automation

Machine Learning Engineer

Torrance, CA · On-site

$160K - $250K/yr

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling these systems end to end. What You'll Do: * Research, develop and deploy cutting-edge deep learning ...

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

Pomona, CA

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 ...

Bespoke Labs

Machine Learning Engineer

Pomona, 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 ...

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Showing results 21-40

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

Embedded Machine Learning Engineer information

See Pasadena, CA salary details

$76.4K

$167.3K

$189.8K

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 Pasadena, CA is $167,311.00, according to ZipRecruiter salary data. Most workers in this role earn between $143,400.00 and $188,700.00 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 Pasadena, CA? For Embedded Machine Learning Engineer jobs in Pasadena, CA, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Pasadena, CA look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Pasadena, CA are:
What cities near Pasadena, CA are hiring for Embedded Machine Learning Engineer jobs? Cities near Pasadena, 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 Pasadena, CA as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $167,311 per year, or $80.4 per hour.

Machine Learning Engineer

Socket.dev

Los Angeles, CA • On-site

$180 - $250/hr

Other

Posted 4 days ago

Job description

Company Description

nowfluence is building the trusted infrastructure layer for influencer marketing.

Backed by USC and Techstars, we're creating the software, data, and intelligence systems that help brands discover creators, launch campaigns, track ROI, manage payments, and make smarter investment decisions across the creator economy.

Our mission is simple: make influencer marketing measurable, scalable, and accessible to everyone. From global consumer brands to local restaurants, cafés, and emerging businesses, we're building the platform that helps companies confidently invest in creator partnerships.

As creator marketing becomes one of the largest advertising channels in the world, we're building the systems that power it.

Role Description

Founding Data Scientist / Machine Learning Engineer

We're looking for a highly ambitious Data Scientist to help build the predictive intelligence layer behind nowfluence.

This is not a reporting or dashboard role.

You'll be responsible for developing the models, pipelines, and analytics systems that help brands answer critical questions:

  • Which creators are most likely to drive revenue?
  • Which partnerships should be renewed?
  • How can campaign outcomes be predicted before budgets are spent?
  • How do we make influencer marketing more accessible and successful for SMBs?

You'll work across product, engineering, and data to transform creator, campaign, audience, and attribution data into scalable machine learning systems used by brands running influencer programs at scale.

Your work will directly influence how businesses allocate marketing budgets and make decisions across the creator economy.

Requirements
  • Strong experience with Python, Machine Learning, and Predictive Modeling
  • Experience building production-grade data pipelines and analytics infrastructure
  • Proficiency with React, Next.js, TypeScript, and Tailwind CSS
  • Experience with PostgreSQL, APIs, and modern cloud architectures
  • Strong understanding of statistics, experimentation, and large-scale data analysis
  • Experience deploying ML models into production environments
  • Startup mindset with a desire to build and move quickly
Bonus
  • Recommendation Systems
  • Marketing Attribution
  • Shopify Data
  • Creator Economy Experience
  • LLMs and AI Agents
  • Data Warehousing & ETL Systems

Join us as we build the intelligence layer powering the future of influencer marketing.

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