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Online Data Encoder Jobs in California (NOW HIRING)

Aquent

Senior Lead ML Encoder

South San Francisco, CA · On-site

$91.09 - $101.21/hr

Are you a visionary machine learning leader passionate about building foundational data ... We also offer free online training through Aquent Gymnasium . More information on our awesome ...

FM Industries

Programmer, Lathe 2

Fremont, CA · On-site

$120K - $135K/yr

Health Spending Plans * Comprehensive Online Learning Center - houses thousands of training ... Analyze manufacturing part drawings, part specifications, GD&T tolerances, and design data to ...

Mem0

Research Engineer - Agent Memory

San Francisco, CA · On-site

$175K - $250K/yr

Model training/fine-tuning experience (LLMs/encoders) with a strong footing in experimental design ... online tests). * Able to orchestrate data pipelines to run these models in production with low ...

Highmark Health

Coding Coordinator

Sacramento, CA · On-site

$37.14/hr

... encoder, testing and upgrades. ESSENTIAL RESPONSIBILITIES: * Reviews dashboards and reports, to ... Processes, records, and trends data. (10%) * Monitors, triages, enters and tracks IT tickets. (15%

Engram

Research Scientist

San Francisco, CA · On-site

Synthetic data and self-study - understanding what makes synthetic training data generalize, and ... RL and online training - exploring reinforcement learning methods that let models improve from ...

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Online Data Encoder information

What is an online data encoder?

An Online Data Encoder is responsible for inputting, updating, and maintaining digital information in a database or system. This role requires accuracy, speed, and attention to detail to ensure that data is correctly recorded and organized. Encoders often work with spreadsheets, company databases, or specialized software to process large amounts of information efficiently. The job may also involve verifying data for errors and ensuring confidentiality and security of sensitive information.

What types of data does an online data encoder typically handle?

As an Online Data Encoder, you will usually work with a variety of information, including customer records, sales figures, survey responses, or inventory logs, depending on the employer and industry. Your primary responsibility is to accurately input, update, and manage this data in digital databases or spreadsheets, often under set deadlines. You may also be asked to review and verify information for accuracy or help generate periodic reports. The type of data you handle can influence the specific procedures you follow and may require you to learn unique data management software or adhere to confidentiality standards. Being diligent and organized will help ensure data integrity and contribute to the smooth operations of your team.

What are the key skills and qualifications needed to thrive as an online data encoder?

To thrive as an Online Data Encoder, strong typing skills, attention to detail, and basic computer literacy are essential, often supported by a high school diploma or equivalent. Familiarity with spreadsheet programs (such as Microsoft Excel or Google Sheets), data management systems, and sometimes basic database software is preferred. Reliability, time management, and the ability to maintain focus during repetitive tasks are standout soft skills for this position. These competencies are crucial for ensuring the accuracy, security, and efficiency of data entry operations in a remote or online work environment.

How much do online data encoders typically make?

Online data encoders typically earn between $10 and $20 per hour, depending on experience, skill level, and the complexity of the data being processed. Some may work freelance or part-time, which can affect overall earnings, and proficiency with data entry tools can improve pay rates.

How to become an online data encoder?

To become an online data encoder, you typically need a high school diploma or equivalent and strong typing, computer, and data entry skills. Familiarity with data management software and attention to detail are important, and some positions may require basic knowledge of spreadsheets or database tools. Gaining experience through online courses or certifications can improve job prospects in this field.

What are the most commonly searched types of Data Encoder jobs in California?

The most popular types of Data Encoder jobs in California are:

What are popular job titles related to Online Data Encoder jobs in California?

For Online Data Encoder jobs in California, the most frequently searched job titles are:

What job categories do people searching Online Data Encoder jobs in California look for?

The top searched job categories for Online Data Encoder jobs in California are:

What cities in California are hiring for Online Data Encoder jobs?

Cities in California with the most Online Data Encoder job openings:

Online Data Encoder jobs near you
Infographic showing various Online Data Encoder job openings in California as of September 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 100% In-person job distribution.

Senior Lead ML Encoder

South San Francisco, CA • On-site

Aquent
Recruiting and Staffing Services • 5 - 10K employees
Learn more about Aquentexternal

$91.09 - $101.21/hr

Temporary

Medical, Dental, Vision, Retirement

Posted 15 days ago

Job description

Placement Type:
Temporary
Salary:
$91.09-101.21 Hourly
Start Date:
Sep 21, 2026
Aquent is proud to partner with a leading biotechnology company that stands at the forefront of scientific discovery, dedicated to improving human health globally. This organization leverages cutting-edge research and innovative technologies to develop life-changing therapies, making a profound impact on patients' lives through data-driven insights and advanced analytical capabilities.
Are you a visionary machine learning leader passionate about building foundational data representations that unlock unprecedented insights? We are seeking an exceptional individual to spearhead the design and execution of a groundbreaking, comprehensive customer representation model. In this high-impact, temporary opportunity, you will be instrumental in training a dense vector embedding on extensive longitudinal transaction and interaction history. Your work will directly power next-generation generative AI and advanced analytics products across the entire enterprise, fundamentally transforming how our client understands and serves its customers. This is your chance to pioneer a first-of-its-kind solution, driving innovation and making a tangible difference in the biotechnology space.
Key Responsibilities
  • Design pretraining objectives and build self-supervised or contrastive representations using longitudinal customer transaction, sales, and interaction data.
  • Develop, train, and evaluate end-to-end encoder models utilizing PyTorch or JAX, ensuring scalable distributed data processing.
  • Establish robust, rigorous evaluation frameworks, including time-based splits, leakage detection, cold-start slices, and transferability to held-out populations.
  • Write clean, production-grade Python code to transition models from research into reliable, versioned, and monitored training and serving pipelines.
  • Judge downstream embedding signal by measuring calibration, stability, drift, and subgroup performance to determine if models deliver incremental value.
  • Present research findings, model capabilities, and trade-offs to senior stakeholders, providing data-driven recommendations on whether to advance or halt specific modeling approaches.

Must-Have Qualifications
  • Hands-on experience designing pretraining objectives and training encoder/embedding models from scratch (not just fine-tuning or consuming pretrained models).
  • Deep expertise in representation learning techniques, such as contrastive learning, temporal sequence modeling, Transformers, Graph Neural Networks (GNNs), or recommender embeddings.
  • Proven track record modeling large, sparse, longitudinal event data (e.g., transactions, customer journeys, clickstreams, or engagement histories).
  • Experience building inductive representations that generalize to entities with minimal history using their own features rather than static lookup tables.
  • Exceptional evaluation hygiene, including time-based splitting, leakage detection, uncertainty estimation, and hard baseline comparisons.
  • Strong engineering expertise in Python with PyTorch or JAX, SQL, distributed data frameworks, and cloud-based training at scale.
  • Demonstrated ability to carry models into production, including setting up data contracts, versioning, serving pipelines, and monitoring.
  • Strong communication skills with the confidence to articulate uncertainty and recommend stopping viable approaches when signal is insufficient.

Nice-to-Have Qualifications
  • Direct experience building comprehensive customer representations, behavioral embeddings, or foundation models over event data.
  • Familiarity with privacy, fairness, and re-identification risks in learned representations of individuals.
  • Relevant publications, patents, or public applied work in representation learning.
  • Prior experience working in high-volume behavioral data environments (e.g., consumer tech, financial services, payments, adtech, or marketplaces).

About Aquent Talent
Aquent Talent connects the best talent in marketing, creative, and design with the world's biggest brands.
Our eligible talent get access to amazing benefits like subsidized health, vision, and dental plans, paid sick leave, and retirement plans with a match. We also offer free online training through Aquent Gymnasium . More information on our awesome benefits !
Aquent is an equal-opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics. We're about creating an inclusive environment-one where different backgrounds, experiences, and perspectives are valued, and everyone can contribute, grow their careers, and thrive.
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