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Machine Learning Engineer Jobs in Riverside, CA (NOW HIRING)

Qcells

Applied AI Engineer

Irvine, CA

$160K - $190K/yr

Train, fine-tune, validate, and optimize machine learning models for performance, scalability, and ... Collaborate with data engineers to collect, preprocess, and clean structured and unstructured data ...

Qcells

Applied AI Engineer

Irvine, CA · On-site

$160K - $190K/yr

Train, fine-tune, validate, and optimize machine learning models for performance, scalability, and ... Collaborate with data engineers to collect, preprocess, and clean structured and unstructured data ...

SynergisticIT

Java/Backend Developer - Entry Level

Irvine, CA · On-site

$54.25 - $70.25/hr

Currently, We are looking for entry-level software programmers, Java full-stack developers, Python/Java developers, Data analysts/ Data Scientists, and Machine Learning engineers for full-time ...

SynergisticIT

Junior BI Engineer

Irvine, CA · On-site

Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers for full time positions ...

Corvel

Data Scientist II

Irvine, CA · On-site +1

$82K - $127K/yr

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field; Master's preferred * 2-5+ years of experience in data science, machine learning, or ...

Pwc

CTIO AI Engineering Manager

Irvine, CA · On-site

$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 ...

Corvel

Data Scientist II

Irvine, CA · On-site +1

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field; Master's preferred * 2-5+ years of experience in data science, machine learning, or ...

CorVel Corporation

Data Scientist II

Irvine, CA · On-site +1

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field; Master's preferred * 2-5+ years of experience in data science, machine learning, or ...

Cotality

Director, ML Engineering

Irvine, CA · On-site

We are seeking a visionary Director of Machine Learning Engineering to lead a high-performing team of ML engineers and MLOps specialists. This leader will bridge the gap between data science and ...

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Showing results 1-20

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Riverside, CA

Machine Learning Engineer information

See Riverside, CA salary details

$32.9K

$134.3K

$201.9K

How much do machine learning engineer jobs pay per year?

As of Jun 26, 2026, the average yearly pay for machine learning engineer in Riverside, CA is $134,341.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,900.00 and $161,700.00 per year, depending on experience, location, and employer.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Riverside, CA? The most popular types of Machine Learning Engineer jobs in Riverside, CA are:
What are popular job titles related to Machine Learning Engineer jobs in Riverside, CA? For Machine Learning Engineer jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Riverside, CA look for? The top searched job categories for Machine Learning Engineer jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Machine Learning Engineer jobs? Cities near Riverside, CA with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Riverside, CA as of June 2026, with employment types broken down into 1% As Needed, 90% Full Time, 5% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $134,341 per year, or $64.6 per hour.

Staff ML Systems Engineer, Distributed Systems

FieldAI

Irvine, CA • On-site

Full-time

Posted 27 days ago

Job description

Job Summary:
FieldAI is a company that specializes in building reliable, field-ready AI systems for robotics. They are seeking a Senior / Staff ML Systems Engineer to architect and build distributed infrastructure for large-scale machine learning workflows, focusing on scalable systems that support data processing and model training.
Responsibilities:
• Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows.
• Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms.
• Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development.
• Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability.
• Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency.
• Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development.
• Establish best practices and engineering standards for distributed machine learning infrastructure.
• Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs.
• Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
Qualifications:
Required:
• 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems.
• Strong Python programming skills, including experience with concurrency, performance optimization, and systems development.
• Experience with distributed computing frameworks such as Ray, Spark, Dask, Flink, or similar technologies.
• Experience designing and scaling data pipelines or machine learning workflows.
• Strong system design skills with demonstrated expertise in scalability, reliability, and performance optimization.
• Experience diagnosing and resolving bottlenecks in distributed environments.
• Ability to work cross-functionally and drive technical decisions across multiple teams.
Preferred:
• Experience building infrastructure for machine learning training and inference systems.
• Familiarity with modern ML frameworks such as PyTorch or TensorFlow.
• Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies.
• Experience operating Kubernetes-based infrastructure and large-scale cloud systems.
• Deep understanding of distributed systems concepts including data locality, serialization costs, scheduling, and resource management.
• Experience with distributed debugging, observability, and workflow orchestration platforms.
• Proven ability to establish technical direction and influence architecture across organizations.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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