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Data Preprocessing Jobs in California (NOW HIRING)

Build and maintain data preprocessing and data generation pipelines to support model training and evaluation. * Run training and fine-tuning workflows end-to-end and iterate quickly on performance ...

On-Device Machine Learning Engineer

Sunnyvale, CA · On-site

$164K/yr

Experience with machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment. Foundational understanding of machine learning: MultiModal LLMs ...

... scale data collection, curation, preprocessing, and management, and implement on-device ML integration systems that deploy state-of-the-art algorithms to Apple devices. Working closely with ML ...

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Data Preprocessing information

What is the highest paying job in data?

In data-related fields, roles such as Data Science Director, Machine Learning Engineer, and Chief Data Officer tend to have the highest salaries, often exceeding six figures annually. These positions typically require advanced skills in data analysis, programming, and leadership, along with extensive experience and relevant certifications.

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.

Is 40 too late for data science?

Data preprocessing is a key step in data science, and individuals can enter the field at any age. Many data scientists start later in life, and acquiring skills in programming, statistics, and tools like Python or R can facilitate entry regardless of age.

What do you do in data preprocessing?

Data preprocessing involves cleaning and transforming raw data to prepare it for analysis or modeling. This includes tasks such as handling missing values, removing duplicates, normalizing data, and encoding categorical variables, often using tools like Python or R. It is a crucial step to ensure data quality and improve model performance.

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.

Will AI replace data analysts?

AI is transforming data analysis by automating routine tasks such as data cleaning and basic reporting, but data analysts are still essential for interpreting complex insights, making strategic decisions, and applying domain knowledge. The role is evolving to include skills in machine learning tools and programming languages like Python or R, but human expertise remains critical for nuanced analysis and contextual understanding.

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 job categories do people searching Data Preprocessing jobs in California look for? The top searched job categories for Data Preprocessing jobs in California are:
What cities in California are hiring for Data Preprocessing jobs? Cities in California with the most Data Preprocessing job openings:
Infographic showing various Data Preprocessing job openings in California as of June 2026, with employment types broken down into 42% Internship, and 58% Full Time. Highlights an 100% In-person job distribution.

ML Engineer

UniversalAGI

San Francisco, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted yesterday


Key responsibilities

  • Build and maintain data preprocessing and data generation pipelines to support model training and evaluation.

  • Run training and fine-tuning workflows end-to-end and iterate quickly on performance improvements.

  • Design and execute benchmarking and evaluation suites to measure progress and customer outcomes.


Job description

San Francisco | Work Directly with CEO & founding team | Report to CEO | OpenAI for Physics | 5 Days Onsite
Machine Learning Engineer
Location: Onsite in San Francisco
Compensation: Competitive Salary + Equity
Who We Are
UniversalAGI is building OpenAI for Physics. AI startup based in San Francisco and backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico). We're building foundation AI models for physics that enable end-to-end industrial automation from initial design through optimization, validation, and production. We're building a high-velocity team of relentless researchers and engineers that will define the next generation of AI for industrial engineering. If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you.
About the Role
UniversalAGI is hiring an ML Engineer to help ship ML outcomes by owning the execution layer: data preprocessing/generation, training/fine-tuning, benchmarking, and delivering results.
What You'll Do
  • Build and maintain data preprocessing and data generation pipelines to support model training and evaluation.
  • Run training and fine-tuning workflows end-to-end and iterate quickly on performance improvements.
  • Design and execute benchmarking/evaluation suites to measure progress and customer outcomes.
  • Collaborate with PhD expert researchers to operationalize model architectures into repeatable, production-grade workflows.
  • Communicate results clearly (metrics, dashboards, short writeups) and maintain high-quality, reproducible work.

Qualifications
  • Strong software engineering skills (clean code, debugging, reliability, reproducibility).
  • Solid ML foundations and hands-on experience with the ML lifecycle: data → training/fine-tuning → evaluation/benchmarking.
    • Prior experience training or fine-tuning models (any modality/type - LLMs, computer vision, physics, surrogate models, etc.)
  • Olympic athlete mindset: You have high standards for yourself and are obsessed with measurable improvement on the metrics you are delivering.
  • Resourcefulness: you know when to do the "quick & correct" fix vs. when to invest in a robust solution, and you can justify the tradeoff with impact/
  • Ownership: Comfortable owning work end-to-end and being accountable for measurable outcomes.

Bonus Qualifications
  • Experience building data pre-processing pipelines for training ML models.
  • Experience with benchmarking methodology, experiment design, and metric selection.
  • Familiarity with distributed training / scalable compute workflows.
  • Experience in an FDE-style / delivery execution role (or similar "ship results fast" environments).

Cultural Fit
  • Technical Respect: Ability to earn respect through hands-on technical contribution
  • Intensity: Thrives in our unusually intense culture - willing to grind when needed
  • Customer Obsession: Passionate about solving real customer problems, not just publishing papers
  • Deep Work: Values long, uninterrupted periods of focused work over meetings
  • High Availability: Ready to be deeply involved whenever critical issues arise
  • Communication: Can translate complex model decisions to customers and team
  • Growth Mindset: Embraces the compounding returns of intelligence and continuous learning
  • Startup Mindset: Comfortable with ambiguity, rapid change, and wearing multiple hats
  • Work Ethic: Willing to put in the extra hours when needed to hit critical milestones
  • Team Player: Collaborative approach with low ego and high accountability
  • Bias for Action: Ships experiments fast, learns from failures, and iterates quickly

What We Offer
  • Opportunity to define the future of physics AI from the ground up
  • Work on cutting-edge problems at the intersection of deep learning and physics simulation
  • Direct collaboration with the founder & CEO and ability to influence company strategy
  • Competitive compensation with significant equity upside
  • In-person first culture - 5 days a week in office with a team that values face-to-face collaboration
  • Access to world-class investors and advisors in the AI space

Benefits
We provide great benefits, including:
  • Competitive compensation and equity.
  • Competitive health, dental, vision benefits paid by the company.
  • 401(k) plan offering.
  • Flexible vacation.
  • Team Building & Fun Activities.
  • Great scope, ownership and impact.
  • AI tools stipend.
  • Monthly commute stipend.
  • Monthly wellness / fitness stipend.
  • Daily office lunch & dinner covered by the company.
  • Immigration support.

How We're Different
"The credit belongs to the man who is actually in the arena, whose face is marred by dust and
sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the
best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least
fails while daring greatly." - Teddy Roosevelt
At our core, we believe in being "in the arena. " We are builders, problem solvers, and risk-takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful.
If you're ready to train the models that will revolutionize physics simulation, push the boundaries of what AI can learn, and deliver real impact, UniversalAGI is the place for you.