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Machine Learning Data Associate Jobs in Encinitas, CA

Waymo

Senior Machine Learning Engineer, Robotics

San Diego, CA · On-site

$110K - $152K/yr

The Sensor Foundations Team's purpose is to provide cleaned-up sensor data through highly optimized ... For this role, we are looking for a strong Software Engineer with robotics and machine learning ...

Qualcomm

Senior Engineer - Machine Learning

San Diego, CA · On-site

$110K - $152K/yr

Improve data quality and model reliability through systematic evaluation Cross-functional ... Machine learning fundamentals (supervised, unsupervised, deep learning) * Transformer architectures ...

Cellanome

Machine Learning Engineer

San Diego, CA · On-site

$160K - $215K/yr

The Machine Learning Engineer will work in close collaboration with the core instrument, assay and software teams to develop algorithms for data analysis and workflow automation. This role reports to ...

Qualcomm

Senior Engineer - Machine Learning

San Diego, CA · On-site

$110K - $152K/yr

Improve data quality and model reliability through systematic evaluation Cross-functional ... Machine learning fundamentals (supervised, unsupervised, deep learning) * Transformer architectures ...

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All JobsMachine Learning Data Associate JobsMachine Learning Data Associate Jobs in Encinitas, CA

Machine Learning Data Associate information

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How much do machine learning data associate jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for machine learning data associate in Encinitas, CA is $20.13, according to ZipRecruiter salary data. Most workers in this role earn between $16.54 and $21.44 per hour, depending on experience, location, and employer.

What is a machine learning data associate?

Machine Learning Data Associates are professionals who support the development of machine learning models by preparing, labeling, and validating data sets. Their work ensures that data used for training algorithms is accurate, consistent, and properly annotated. They may also assist with data cleaning, quality checks, and sometimes basic data analysis tasks. This role is crucial in industries where high-quality labeled data is essential for building effective AI systems.

What are the key skills and qualifications needed to thrive as a machine learning data associate?

To thrive as a Machine Learning Data Associate, you need strong analytical skills, attention to detail, and a basic understanding of data annotation and labeling processes, often supported by a degree in computer science or a related field. Familiarity with data management tools, annotation platforms, and sometimes scripting languages like Python is typically required. Strong communication, collaboration, and problem-solving abilities help you work efficiently with data science teams and ensure high-quality outcomes. These skills and qualities are crucial for producing accurate datasets that directly impact the effectiveness of machine learning models.

How does a machine learning data associate typically collaborate with data scientists and engineers within a project team?

As a Machine Learning Data Associate, you play a vital role in supporting data scientists and engineers by annotating, cleaning, and organizing large datasets to ensure high data quality. You'll frequently communicate with team members to clarify labeling guidelines, provide feedback on data inconsistencies, and report any edge cases encountered during annotation. This collaboration ensures that the datasets used for training machine learning models are accurate and comprehensive, directly impacting the success of the project. Expect regular team meetings and ongoing feedback loops to maintain alignment with evolving project requirements.

What is the difference between Machine Learning Data Associate vs Data Analyst?

AspectMachine Learning Data AssociateData Analyst
Required SkillsData cleaning, labeling, basic programming, understanding of ML workflowsData interpretation, visualization, statistical analysis
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing, healthcare sectors
Common CertificationsData Science certifications, Python, SQLExcel, Tableau, SQL certifications

The main difference is that Machine Learning Data Associates focus on preparing and labeling data specifically for machine learning models, while Data Analysts interpret data to generate insights for business decisions. Both roles require strong data skills and often overlap, but their primary objectives and work environments differ.

How do I become a machine learning data associate?

To become a machine learning data associate, candidates typically need a high school diploma or equivalent, with some roles preferring a bachelor's degree in computer science, data science, or related fields. Relevant skills include data annotation, understanding of machine learning concepts, and proficiency with tools like Excel, SQL, or data labeling platforms. Gaining experience through internships or certifications can improve job prospects in this field.

Is a Machine Learning Data Associate a good job?

A Machine Learning Data Associate role involves preparing and managing data for machine learning models, often requiring skills in data cleaning, annotation, and familiarity with tools like Python or SQL. It can be a good entry-level position for those interested in AI and data science, offering opportunities to develop technical skills and gain industry experience. Compensation and job satisfaction vary depending on the employer and location, but it generally provides a solid foundation for a career in machine learning or data analysis.

What cities near Encinitas, CA are hiring for Machine Learning Data Associate jobs?

Cities near Encinitas, CA with the most Machine Learning Data Associate job openings:

Machine Learning Data Associate jobs near you
Infographic showing various Machine Learning Data Associate job openings in Encinitas, CA as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 18% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $41,862 per year, or $20.1 per hour.

Staff Machine Learning Engineer, Data Flywheel

San Diego, CA • Hybrid

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

$121K - $146K/yr

Full-time

Re-posted yesterday

Job description

The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that "perceives" the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware.

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

You will:

  • Create large scale data sets and training recipes, develop methods and recipes for human and machine labeling of data sets
  • Develop methods for data mining and recipes for automated data collection/model update flywheels
  • Develop methods and recipes for evaluating real-world performance of models, and detecting regressions in model updates
  • Understand the data needs of the problem domain team and design scalable infra solutions that support model improvement and product expansion.
  • Design, build and implement ML data infra and validate the changes to support the continuing scaling of VLM data needs.
  • Collaborate with ML infrastructure teams and the problem domain team to address issues and bottlenecks and streamline validation. 

You have:

  • A degree in Computer Science, Engineering, or a related technical field
  • 4+ years of professional experience in the field of software engineering and machine learning
  • Proficiency in C++ and Python
  • Experience in designing distributed systems processing data at scale, especially ML data infra
  • Good foundational understanding of ML principles and SOTA methods
  • Passionate about building world-class ML infrastructure
  • Strong communication skills

We prefer:

  • Experience with implementing data compliance & data governance solutions
  • Experience with VLM/LLMs

Apply