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Machine Learning Ops Engineer Jobs in California

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

$122.80K - $184.20K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

... machine learning/deep learning systems, computer vision, graphics, computational imaging applications.Experience with Pytorch. MS/PhD in computer vision, electrical, optical or computer engineering ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

$125.60K - $234.15K/yr

Implement ML-Ops best practices to ensure scalable, reliable, and efficient machine learning ... Proficiency in one or more programming languages such as Python, Scala, Java, or SQL. * Proficiency ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Adobe

Machine Learning Engineer

San Jose, CA · On-site

Adobe is at the forefront of driving digital transformation and is seeking a Machine Learning Engineer to develop machine learning models and algorithms. The role involves collaborating with multi ...

Capital One

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120.80K - $159.10K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this ...

Adobe

Machine Learning Engineer

San Jose, CA

$125.60K - $234.15K/yr

Implement ML-Ops best practices to ensure scalable, reliable, and efficient machine learning ... Proficiency in one or more programming languages such as Python, Scala, Java, or SQL. * Proficiency ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

Machine Learning Engineer The Opportunity Join Adobe and be at the forefront of driving digital transformation. As a Machine Learning Engineer, you will play a key role in developing machine learning ...

Apple

Machine Learning Engineer

Cupertino, CA · On-site

As a Machine Learning Engineer, you will design and build cutting-edge AI/ML systems that drive meaningful business outcomes at scale. You will work cross-functionally to bring innovative machine ...

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Machine Learning Ops Engineer information

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$31.1K

$127.1K

$191K

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

As of May 29, 2026, the average yearly pay for machine learning ops engineer in California is $127,083.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,200.00 and $153,000.00 per year, depending on experience, location, and employer.

What is a Machine Learning Ops Engineer job?

A Machine Learning Ops Engineer (MLOps Engineer) focuses on deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and software engineering, ensuring models run efficiently, reliably, and at scale. Their responsibilities include automating workflows, managing infrastructure, and ensuring CI/CD pipelines for ML models. They work with tools like Kubernetes, Docker, and cloud platforms to streamline model deployment. Ultimately, an MLOps Engineer ensures that machine learning models are operationalized and continuously improved in a real-world environment.

What are the key skills and qualifications needed to thrive in the Machine Learning Ops Engineer position, and why are they important?

To thrive as a Machine Learning Ops Engineer, you need a solid grasp of machine learning concepts, cloud platforms, software engineering, and DevOps practices, typically supported by a degree in computer science or a related field. Experience with tools like Docker, Kubernetes, TensorFlow, CI/CD pipelines, and certifications such as AWS Certified Machine Learning – Specialty are highly valuable. Strong problem-solving skills, communication, and the ability to work collaboratively across data science and engineering teams set top candidates apart. These skills ensure reliable deployment, scalability, and optimization of machine learning models in production environments.

What does a typical day look like for a Machine Learning Ops Engineer?

A typical day for a Machine Learning Ops Engineer involves collaborating with data scientists to streamline the deployment of models, building and maintaining scalable infrastructure on cloud services, and automating workflows with CI/CD tools. You may troubleshoot issues in production environments, monitor model performance, and implement solutions for model versioning and retraining. Often, you’ll work closely with software engineers, DevOps teams, and data analysts to ensure seamless integration of machine learning solutions into products. This cross-functional role keeps you engaged with cutting-edge technology and provides opportunities to influence both technical and business outcomes.
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Machine Learning Ops Engineer jobs near you
Infographic showing various Machine Learning Ops Engineer job openings in California as of May 2026, with employment types broken down into 90% Full Time, 6% Part Time, and 4% Contract. Highlights an 40% Physical, 20% Hybrid, and 40% Remote job distribution, with an average salary of $127,083 per year, or $61.1 per hour.
Senior Manager, Machine Learning Ops Engineering - Automotive

Senior Manager, Machine Learning Ops Engineering - Automotive

Nvidia Corporation

Santa Clara, CA • On-site

Full-time

Posted 8 days ago

Job description

NVIDIA is seeking a Senior MLOps Engineering Manager to join our Autonomous Driving organization in Santa Clara, CA. This role offers an outstanding opportunity to lead the build, development, and operation of large-scale, end-to-end data and ML pipelines that power NVIDIA's autonomous driving products. You will lead a highly technical engineering team responsible for building and operating cloud-scale pipelines that ingest, validate, process, and transform extensive volumes of multimodal sensor data-including camera, lidar, and radar-into high-quality training, evaluation, and validation datasets. These pipelines are foundational to NVIDIA's AV program and directly enable customer-facing autonomy features. We want a seasoned engineering leader with strong ownership and passion for customer-focused development. This person will scale systems and teams in a fast paced, multi-functional environment.
What You Will Be Doing:
  • Lead and grow a high-performing MLOps engineering group tasked with managing end-to-end data pipelines supporting NVIDIA's autonomous driving technology from levels L2 through L4.
  • Own the architecture, execution, and operational excellence of large-scale, cloud-native pipelines for multimodal sensor data ingestion, processing, labeling, and validation.
  • Drive the development of robust, scalable, and observable MLOps systems that support model training, ground truth generation, and continuous evaluation at AV scale.
  • Partner closely with perception, ML, data labeling, infrastructure, and product teams to translate customer and program requirements into reliable production systems.
  • Define technical vision, roadmap, success metrics, and operational benchmarks, and ensure consistent execution against program achievements.
  • Champion customer-first thinking and ownership, ensuring the systems your team builds directly deliver measurable value to internal and external AV customers.
  • Balance hands-on technical depth with people leadership, providing technical guidance, mentorship, and career development for senior engineers and managers.
  • Operate across multiple layers of the stack, including Python, C++, distributed systems, cloud infrastructure, CI/CD, and data platforms.

What We Need to See:
  • Bachelor's or equivalent experience, Master's, or PhD in Computer Science, Electrical Engineering, or a closely related field (or equivalent experience).
  • 10+ overall years of overall engineering experience, including crafting and coordinating production-grade distributed systems.
  • 5+ years of engineering management experience, with a proven history of guiding teams delivering sophisticated, large-scale systems.
  • Strong background in MLOps, data pipelines, and cloud-based distributed systems.
  • Proficiency in Python and C++, with the ability to guide system-level and performance-critical build decisions.
  • Experience crafting and operating end-to-end data or ML pipelines with high reliability, scale, and observability.
  • Prior experience in one or more of the following domains: Autonomous Vehicles, Robotics, Computer Vision, Deep Learning, or GPU-accelerated computing.
  • Excellent communication and leadership skills, capable of aligning collaborators and driving execution in a multi-functional organization.
  • Demonstrated passion for ownership, accountability, and engineering that prioritizes customers.

Ways to Stand Out from the Crowd:
  • Experience developing and leading AV-scale data platforms handling petabyte-scale sensor data.
  • Strong background of leading teams responsible for production MLOps or data infrastructure.
  • Experience with automotive or robotic systems, including real-world sensor data pipelines.
  • Background in distributed cloud systems, workflow orchestration, and large-scale CI/CD.
  • Familiarity with 3D geometry, perception pipelines, or data generation based on simulated environments.

NVIDIA is widely considered one of the most sought-after employers in the technology industry. We offer highly competitive compensation along with an extensive benefits plan. Our work enables new universes of discovery-from artificial intelligence to autonomous vehicles-and brings once-science-fiction technologies into reality. If you are passionate about autonomous driving, scaling complex systems, and leading teams that deliver real customer impact, we would love to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until March 27, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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