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Machine Learning Operations Jobs in El Cajon, CA

Intuit

Senior Machine Learning Engineer

San Diego, CA

$171K - $231K/yr

Senior Machine Learning Engineer Join a vibrant team of machine learning engineers helping conceive ... What You'll Do You own the technical outcomes of your projects and their operational excellence ...

Intuit

Senior Machine Learning Engineer

San Diego, CA

$171K - $231K/yr

Overview Senior Machine Learning Engineer Join a vibrant team of machine learning engineers helping ... What You'll Do You own the technical outcomes of your projects and their operational excellence ...

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All JobsMachine Learning Operations JobsMachine Learning Operations Jobs in El Cajon, CA

Machine Learning Operations information

See El Cajon, CA salary details

$22

$41

$63

How much do machine learning operations jobs pay per hour?

As of Aug 31, 2026, the average hourly pay for machine learning operations in El Cajon, CA is $41.65, according to ZipRecruiter salary data. Most workers in this role earn between $34.90 and $44.18 per hour, depending on experience, location, and employer.

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

AspectMachine Learning OperationsData Scientist
Primary FocusDeploying, maintaining, and scaling ML models in productionAnalyzing data to develop insights and build models
Required SkillsML deployment, cloud platforms, automation, scriptingStatistical analysis, data visualization, programming (Python/R)
Work EnvironmentOperations teams, cloud infrastructure, production systemsResearch environments, data analysis teams, R&D
Common CertificationsCloud certifications, MLOps tools certificationsData science certifications, statistical courses

Machine Learning Operations and Data Scientists often collaborate, but MLOps focuses on deploying and maintaining models in production, while Data Scientists focus on analyzing data and developing models. Both roles require technical skills, but their day-to-day tasks and environments differ.

Is machine learning operations a high paying job?

Machine Learning Operations (MLOps) roles typically offer high salaries due to the specialized skills required, such as expertise in cloud platforms, automation, and data engineering. Compensation varies based on experience, location, and company size, but generally ranks among well-paying tech jobs.

What are machine learning operations?

Machine Learning Operations (MLOps) is a set of practices that combines machine learning, software engineering, and DevOps to deploy, monitor, and maintain machine learning models in production environments. It involves tasks such as model versioning, automation, testing, and ensuring scalability and reliability using tools like CI/CD pipelines and cloud platforms.

What cities near El Cajon, CA are hiring for Machine Learning Operations jobs?

Cities near El Cajon, CA with the most Machine Learning Operations job openings:

Machine Learning Operations jobs near you
Infographic showing various Machine Learning Operations job openings in El Cajon, CA as of June 2026, with employment types broken down into 2% As Needed, 42% Full Time, 54% Part Time, and 2% Temporary. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $86,624 per year, or $41.6 per hour.

Perception Machine Learning Engineer - Continuous Learning

San Diego, CA • On-site

Waymo
Internet and IT • 1 - 5K employees
Learn more about Waymoexternal

Full-time

Posted 17 days ago

Job description

As a Perception Machine Learning Engineer, you will build the intelligent systems that "see" the world, directly shaping the future of autonomous travel.

Within the Perception team, we are tackling some of the most complex, open-ended challenges in autonomous driving. Our models must constantly adapt and improve as our fleet encounters the vast, unpredictable realities of public roads. We are looking for a Machine Learning Engineer to help design and build the automated, closed-loop systems that drive this continuous improvement.

In this role, you will be the bridge between model architecture and large-scale data infrastructure. You will leverage active learning and sophisticated data curation strategies to ensure our perception models are always learning from the most informative examples. Crucially, this means managing the entire lifecycle of our data: intelligently selecting novel scenarios from the fleet while continuously pruning our existing corpus to maximize training efficiency.

In this hybrid role you will report to a Technical Lead Manager.

You will:

  • Architect Infrastructure: Design and scale the data pipelines needed to mine, ingest, and manage massive volumes of sensor data from our fleet.
  • Drive Model Improvement: Deploy active learning algorithms to continuously identify and select the most impactful data for training, ensuring our large models continuously adapt to new environments with incremental updates.
  • Ensure Model Quality: Develop methods and recipes for evaluating real-world performance of our models, and detecting regressions in model updates.  Develop and maintain ground-truth free performance metrics.
  • Optimize Data Efficiency: Conduct large-scale experiments focused on data balancing, subset selection, and label quality optimization. Lead automated curation strategies-including smart pruning and downsampling-to minimize dataset bloat and maximize compute efficiency.
  • Solve Long-Tail Challenges: Develop robust mining, training and evaluation pipelines for rare, safety-critical real-world scenarios.
  • Innovate with Model Signals: Utilize uncertainty estimation, confidence scores, and embedding space analysis to uncover model blind spots and guide automated data acquisition.
  • Collaborate Cross-Functionally: Work closely with researchers and operations teams to iterate on the end-to-end model development lifecycle.

You Have:

  • A bachelor's degree in Machine Learning, Robotics, or Computer Science.
    3+ years of professional experience in Machine Learning and/or Computer Vision.
  • Proven, hands-on experience applying active learning in production environments.
  • Strong expertise in building large-scale ML data pipelines (mining, extraction, auto-labeling, ingestion).
  • Deep understanding of data curation-balancing, core set selection, and sampling-to optimize model performance.
  • Proficiency in Python and deep learning frameworks (PyTorch or JAX).
  • Strong software engineering skills for writing robust, production-ready code.

We Prefer:

  • An advanced degree (MS or PhD) in Machine Learning, Robotics, or Computer Science.
  • A record of publications at top-tier conferences (e.g., CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, IROS, RSS, AAAI, IJCV, PAMI).
  • Experience with C++
  • Experience building data-centric infrastructure from the ground up to accelerate model iteration cycles.

Apply