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Machine Learning Ops Engineer Jobs in California

Matterport - Senior ML Ops Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

Analyze and profile machine learning models to identify performance bottlenecks and areas for optimization. Implement and apply model optimization techniques such as quantization, pruning ...

Sr MLop engineer

San Leandro, CA · On-site

$116K - $159K/yr

Syntricate Technologies is seeking a Sr ML Ops Engineer to drive the full lifecycle of machine learning solutions, bridging the gap between data science model development and production-grade ML Ops ...

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 ...

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 ...

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift: N/A Location: San Mateo, CA (Peninsula) - Onsite Preferred Schedule: Full time, Permanent Role Visa ...

... AIML, Machine Learning Model Operations. * Strong proficiency in Java and Python, SQL, and ML ... Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks. * Solid ...

Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/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 ...

... AIML, Machine Learning Model Operations. * Strong proficiency in Java and Python, SQL, and ML ... Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks. * Solid ...

Lead Machine Learning Engineer

San Jose, CA · On-site

$120K - $158K/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 ...

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/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 ...

Machine Learning Engineer IV

Poway, CA · On-site

$140K - $252K/yr

LLM Ops Integration * Design and develop new processes to support the integration of AI/ML ... May substitute equivalent machine learning engineer experience in lieu of education. * Must ...

Showing results 21-40

Machine Learning Ops Engineer information

See California salary details

$31.1K

$127.1K

$191K

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

As of Aug 20, 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?

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 does a machine learning ops engineer do?

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.

What skills and qualifications are needed to be a machine learning ops engineer?

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.

Are machine learning ops engineers in demand?

Machine Learning Ops Engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Kubernetes and TensorFlow. The role is expected to grow as organizations prioritize AI-driven solutions and infrastructure automation.

What are the most commonly searched types of Machine Learning Ops Engineer jobs in California?

The most popular types of Machine Learning Ops Engineer jobs in California are:

What cities in California are hiring for Machine Learning Ops Engineer jobs?

Cities in California with the most Machine Learning Ops Engineer job openings:

Infographic showing various Machine Learning Ops Engineer job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $127,083 per year, or $61.1 per hour.

Senior Manager, Machine Learning Ops Engineering - Automotive

NVIDIA Gruppe

Santa Clara, CA • On-site

$272 - $431.25/hr

Other

Posted 2 days ago

New


Job description

Overview

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.
Compensation & Benefits

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.

EEO Statement

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.

Applications for this job will be accepted at least until March 27, 2026.

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