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Data Preprocessing Jobs in Hercules, CA (NOW HIRING)

... data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for ...

... data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for ...

Take end-to-end ownership of the AI lifecycle -- from data collection and preprocessing to model training, evaluation, and deployment. • Integrate with Observability Stack: Work closely with the ...

AI Biologist - SpatialBench-Long As molecular data generation and frontier model intelligence grows ... You understand how image preprocessing (tissue alignment, background removal) affects segmentation ...

AI Biologist - SpatialBench-Long As molecular data generation and frontier model intelligence grows ... You understand how image preprocessing (tissue alignment, background removal) affects segmentation ...

Developing performant systems for ingesting and preprocessing data on a regular basis for continual training * Building data observability for quality control and reproducibility * Reliable ...

Showing results 41-60

Data Preprocessing information

See Hercules, CA salary details

$50.8K

$182.2K

$268.9K

How much do data preprocessing jobs pay per year?

As of Sep 13, 2026, the average yearly pay for data preprocessing in Hercules, CA is $182,199.00, according to ZipRecruiter salary data. Most workers in this role earn between $147,400.00 and $187,700.00 per year, depending on experience, location, and employer.

What is data preprocessing?

Data preprocessing is the process of cleaning, transforming, and organizing raw data into a usable format for analysis or machine learning. It involves steps such as handling missing values, removing duplicates, normalizing or scaling data, and encoding categorical variables. Proper data preprocessing helps improve the quality and performance of predictive models by ensuring the data is accurate, consistent, and suitable for analysis.

What are the key skills and qualifications needed to thrive as a data preprocessing specialist, and why are they important?

To thrive as a Data Preprocessing Specialist, you need a strong background in statistics, data cleaning, and data transformation, often supported by a degree in computer science, data science, or a related field. Proficiency with tools such as Python (pandas, NumPy), SQL, and data visualization platforms is typically essential, along with familiarity with data management systems. Attention to detail, problem-solving abilities, and effective communication are standout soft skills in this position. These skills are crucial for ensuring high-quality, reliable datasets that underpin accurate data analysis and machine learning outcomes.

What are some common challenges faced in a data preprocessing role, and how can they be effectively managed?

Professionals in Data Preprocessing often encounter challenges such as handling incomplete or inconsistent data, managing large datasets, and ensuring data quality before analysis. Addressing these issues typically involves using specialized tools to automate data cleaning, establishing clear data validation rules, and collaborating closely with data engineers and analysts. Staying updated with best practices and leveraging scripting languages like Python or R can also streamline the preprocessing workflow, making it easier to deliver reliable and accurate datasets for downstream analysis.

What is the difference between Data Preprocessing vs Data Analysis?

AspectData PreprocessingData Analysis
Primary FocusCleaning, transforming, and preparing raw data for analysisInterpreting data to extract insights and support decision-making
Skills RequiredData cleaning, scripting, understanding of data formatsStatistical analysis, data visualization, critical thinking
Work EnvironmentData engineering teams, data science projectsBusiness intelligence, research, data science teams
Tools UsedPython, R, SQL, ETL toolsExcel, Tableau, R, Python, statistical software

While data preprocessing involves preparing raw data for analysis by cleaning and transforming it, data analysis focuses on interpreting the prepared data to uncover trends and insights. Both roles are essential in the data pipeline but serve different purposes in the data lifecycle.

What job categories do people searching Data Preprocessing jobs in Hercules, CA look for?

The top searched job categories for Data Preprocessing jobs in Hercules, CA are:

What cities near Hercules, CA are hiring for Data Preprocessing jobs?

Cities near Hercules, CA with the most Data Preprocessing job openings:

Infographic showing various Data Preprocessing job openings in Hercules, CA as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 16% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $182,199 per year, or $87.6 per hour.

Technical Lead Manager - Training Runtime, Data(set) Movement

San Francisco, CA • On-site

OpenAI
Scientific Research and Development Services • 201 - 500 employees

$380K - $500K/yr

Full-time

Re-posted 6 days ago


Key responsibilities

  • Design and build a unified dataset read platform for multiple current and future training frameworks.

  • Write and review production code in core data loading, service, caching, and reliability paths.

  • Partner with teams working on training frameworks, reinforcement learning, multimodal models, storage, runtime, and cluster infrastructure.


Job description

About the Team
Training Runtime builds the distributed systems that power OpenAI's largest model training runs - most recently GPT-5.5! The Data Movement area owns the infrastructure that keeps training jobs supplied with the right data at the right time, and keeps model state moving safely and efficiently across large clusters.
Our work spans machine learning systems, distributed storage, high-throughput data loading, reliability engineering, and developer experience. Success means researchers can move quickly while training runs remain fast, reproducible, debuggable, and resilient at scale.
About the Role
We are looking for a deeply hands-on Technical Lead Manager to own datasets throughout our training infrastructure. This person will set the direction for how training jobs read data: the APIs, storage contracts, versioning model, benchmarks, debugging tools, and reliability guarantees that make data access consistent across current and future training frameworks.
You will begin as the primary technical owner for dataset reads, working directly in the code while aligning researchers, training framework owners, storage teams, and infrastructure partners around a durable platform. The problem is deceptively hard at frontier scale: make enormous, heterogeneous datasets easy to consume, correct across distributed workers, observable when something goes wrong, and flexible enough to support pretraining, reinforcement learning, and multimodal training.
In this role, you will
  • Design and build a unified dataset read platform for multiple current and future training frameworks.
  • Define dataset APIs, storage-format expectations, registration/versioning, and migration paths that make data access reproducible and maintainable.
  • Build reliability into the read path, including stateful iteration, caching, fast restart, recovery, and clear operational contracts.
  • Build terminal and web-based visualizers that let teams inspect text, multimodal, and reinforcement learning data late in the pipeline, where bugs are most visible.
  • Write and review production code in core data loading, service, caching, and reliability paths.
  • Partner with teams working on training frameworks, reinforcement learning, multimodal models, storage, runtime, and cluster infrastructure.
Over Time
The long-term goal is a team that owns fast, correct, scalable, and reliable in-cluster data movement for training: data that comes in, data that goes out, and data that moves around inside the cluster. After ramping on datasets, this role will expand to TLM ownership for broader data movement systems, including checkpoint loads/saves and snapshot transfers, while partnering closely with existing technical leads and adjacent infrastructure teams.
You might thrive in this role if you:
  • Have built or owned dataset, data loading, storage, or distributed training infrastructure at large scale (e.g. torch.utils.data)
  • Care equally about API design, debugging ergonomics, performance, and bit-level correctness.
  • Understand the failure modes of large distributed training jobs and know how data systems can create or prevent them.
  • Have experience with stateful iterators, checkpoint/restart semantics, caching, remote services, or high-throughput storage reads.
  • Are comfortable working across Python and lower-level systems code; Rust or C++ experience is useful but not required.
  • Have worked with multimodal, video, reinforcement learning, or pretraining data pipelines where small data bugs are expensive and hard to diagnose.
  • Can lead through code and technical judgment before a team exists, and can later manage engineers without losing the hands-on edge.
  • Obsess over developer experience by eliminating friction, such as manual preprocessing scripts and niche cluster-specific bugs, ensuring a reliable and efficient experience for researchers.

About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI's Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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