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Remote Research Jobs in San Rafael, CA (NOW HIRING)

talentpluto

Research Engineer

San Francisco, CA · On-site +1

$140K - $250K/yr

Location: Remote (United States) Work Model: Remote Industry: AI training data infrastructure ... As a Research Engineer, you will build the automated systems that verify and assure data quality so ...

talentpluto

Research Engineer

San Francisco, CA · Remote

$140K - $250K/yr

Location: Remote (United States) Work Model: Remote Industry: AI training data infrastructure ... As a Research Engineer, you will build the automated systems that verify and assure data quality so ...

Nourish

Research Associate

San Francisco, CA · On-site +1

This role is full-time and open to NYC-based or remote candidates. Our office is located in ... Research writing -- Draft white papers and conference abstracts; contribute to peer-reviewed ...

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Remote Research information

What is remote research?

A Remote Research job involves conducting studies, analyzing data, and gathering insights from a remote location, without being physically present in a traditional office. Researchers may work in various fields such as market research, academic studies, or user experience testing. They use digital tools to collect and analyze information, collaborate with teams, and present findings. This role requires strong analytical skills, attention to detail, and proficiency in research methodologies. It offers flexibility but also requires self-discipline and effective communication.

What are the key skills and qualifications needed to thrive in remote research?

To thrive in Remote Research, you need strong analytical, critical thinking, and data analysis skills, often supported by a bachelor’s or master’s degree in a relevant field such as social sciences, market research, or scientific disciplines. Experience with research databases, statistical tools like SPSS or R, and data visualization platforms is highly beneficial. Exceptional written communication, self-motivation, and time management skills distinguish top performers in this remote setting. These abilities ensure high-quality, independent work and reliable research outcomes while collaborating virtually with teams.

What are some common challenges faced in remote research and how can they be addressed?

One of the main challenges in Remote Research positions is maintaining effective communication and collaboration with team members across different locations and time zones. Researchers may also need to independently manage their workload and ensure access to up-to-date resources or data, which requires strong organizational skills. To overcome these obstacles, utilizing collaborative platforms like Slack or Microsoft Teams, maintaining clear written records, and establishing regular check-in meetings are highly effective. Adapting to these remote work dynamics helps build strong partnerships and ensures research projects remain on track and impactful.

What are the most commonly searched types of Research jobs in San Rafael, CA?

The most popular types of Research jobs in San Rafael, CA are:

What are popular job titles related to Remote Research jobs in San Rafael, CA?

For Remote Research jobs in San Rafael, CA, the most frequently searched job titles are:

What job categories do people searching Remote Research jobs in San Rafael, CA look for?

The top searched job categories for Remote Research jobs in San Rafael, CA are:

What cities near San Rafael, CA are hiring for Remote Research jobs?

Cities near San Rafael, CA with the most Remote Research job openings:

Remote Research jobs near you
Infographic showing various Remote Research job openings in San Rafael, CA as of August 2026, with employment types broken down into 4% Internship, 71% Full Time, 15% Part Time, and 10% Contract. Highlights an 100% Remote job distribution.

Research Engineer

talentpluto

San Francisco, CA • On-site, Remote

$140K - $250K/yr

Full-time

Re-posted 6 days ago

Job description

Location: Remote (United States)
Work Model: Remote
Industry: AI training data infrastructure
Compensation: $140K-$250K base, plus equity
About the Company
Our partner is a YC-backed company building a new kind of marketplace in the AI training data space. Rather than operating as a labor marketplace, they provide infrastructure that lets data producers transform their existing data into formats AI labs want and sell it directly to those labs. This democratized model unlocks far more high-value data sources, and the team is growing quickly to keep up with demand.
The Opportunity
This is the company's top hiring priority and a genuinely hard research problem. Because data flows through a decentralized marketplace, ensuring quality at scale is the single biggest bottleneck to growth. As a Research Engineer, you will build the automated systems that verify and assure data quality so that suppliers consistently deliver excellent data to buyers.
You will start by digging into the data manually to understand failure modes, then design systems to automate quality checks at scale, combining rule-based approaches with AI for fuzzier cases and human-in-the-loop review where it makes sense. This is fundamentally a research role focused on building automated systems, not manual QA.
Responsibilities
  • Identify data quality issues including inconsistencies, formatting problems, and ingestion challenges
  • Perform initial manual data quality review to deeply understand failure modes
  • Build systems to automate quality checks at scale using rule-based and AI-driven approaches
  • Design hybrid systems that balance automation with human-in-the-loop review where appropriate
  • Continuously improve verification methods as the data landscape and AI tooling evolve
Requirements
  • Deeply technical, with a strong learning slope and the ability to ramp quickly in a fast-moving field
  • Background in AI/ML engineering, or software engineering at an AI-focused company with visible data ingestion and processing experience
  • Ability to reason about likely data quality problems from first principles
  • Comfortable owning ambiguous, open-ended problems end to end
  • Comfortable working in person, full-time, in a San Francisco office
  • Bonus: experience working with noisy or unstructured data, or judgment on when to use automation versus human-in-the-loop review

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