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Machine Learning Engineer Software Engineer Jobs in California

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Working closely with process engineers, software engineers, and fellow ML engineers, you will ...

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Working closely with process engineers, software engineers, and fellow ML engineers, you will ...

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Working closely with process engineers, software engineers, and fellow ML engineers, you will ...

Minimum Qualifications Software engineering skills and proficiency in Python Experience with ... machine learning, computer science, computer engineering or related fields.

Software engineering skills and proficiency in Python. Experience with PyTorch. BA/BS degree in computer vision, computer graphics, machine learning or related field. Preferred Qualifications MS or ...

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Experience in software engineering with a focus on distributed systems and scalable backend ...

Machine Learning Engineer

San Francisco, CA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Experience in software engineering with a focus on distributed systems and scalable backend ...

We are building an AI-driven simulation software stack for engineering and manufacturing across ... Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who ...

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling advanced software systems to automate Design for Manufacturing analysis. Responsibilities : • ...

Working at the intersection of data science and software engineering, you translate R&D and project ... This Role As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation ...

Strong background in algorithms, data structures, and software engineering principles. * Experience ... Deep understanding of state-of-the-art machine learning techniques and models. * Extensive industry ...

Software engineering skills and proficiency in Python. Experience with PyTorch. BA/BS degree in computer vision, computer graphics, machine learning or related field. Pay & Benefits At Apple, base ...

... Machine Learning Engineer with experience developing ML models for computer vision and graphics ... Minimum Qualifications Software engineering skills and proficiency in Python Experience with ...

Machine Learning Engineer II

Palo Alto, CA · On-site +1

$114K - $156K/yr

Machine Learning Software Engineers who bridge the gap between research and production by delivering machine learning models into real-world product experiences at scale About the Role: We are ...

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Machine Learning Engineer Software Engineer information

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

AspectMachine Learning EngineerSoftware Engineer
Required CredentialsBachelor's/Master's in CS, specialized ML coursesBachelor's in CS or related field
Work EnvironmentDevelops ML models, algorithms, data pipelinesBuilds software applications, systems, APIs
Industry UsageAI/ML projects, data-driven solutionsWeb, mobile, enterprise software

Machine Learning Engineers focus on designing and deploying ML models, requiring expertise in algorithms and data handling. Software Engineers develop broader software applications, emphasizing coding and system architecture. While both roles require programming skills, ML Engineers specialize in AI/ML tasks, whereas Software Engineers work across various software domains.

How do Machine Learning Engineer Software Engineers typically collaborate with data scientists and software development teams?

Machine Learning Engineer Software Engineers often serve as a bridge between data scientists and software development teams. They work closely with data scientists to understand and implement machine learning models, ensuring that the models are production-ready and scalable. Additionally, they collaborate with software engineers to integrate these models into existing applications, monitor their performance, and address any engineering challenges. This cross-functional collaboration is essential for delivering robust, end-to-end AI solutions that add real value to the business.
What cities in California are hiring for Machine Learning Engineer Software Engineer jobs? Cities in California with the most Machine Learning Engineer Software Engineer job openings:
Infographic showing various Machine Learning Engineer Software Engineer job openings in California as of July 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 75% In-person, and 25% Remote job distribution.

Machine Learning Engineer

V-Work Infotech Solutions INC

Cerritos, CA • On-site

Other

This job post has expired today. Applications are no longer accepted.


Job description

Job Summary

We are seeking an experienced Machine Learning Engineer with 10+ years of software engineering or data engineering experience and strong expertise in designing, developing, deploying, and scaling machine learning solutions. The ideal candidate should have hands-on experience with Python, deep learning frameworks, MLOps, cloud platforms, data engineering, and Generative AI technologies.

The candidate will work closely with Data Scientists, AI Engineers, Data Engineers, and business stakeholders to build production-ready ML models and AI-powered applications.


Key Responsibilities
  • Design, build, and deploy machine learning models for production environments.
  • Develop predictive analytics, classification, regression, recommendation, and NLP models.
  • Build end-to-end ML pipelines for data ingestion, feature engineering, training, deployment, and monitoring.
  • Collaborate with Data Engineering teams to build scalable ML workflows.
  • Deploy ML models using Docker, Kubernetes, and cloud platforms.
  • Optimize model performance, scalability, and inference latency.
  • Implement CI/CD pipelines for machine learning (MLOps).
  • Work with structured and unstructured data from multiple sources.
  • Develop APIs for model serving and integration.
  • Monitor model drift, accuracy, and production performance.
  • Document technical designs and collaborate with cross-functional teams.