Distyl

1 month ago

Research Engineers, Data

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Research Engineers design and build data systems powering reliable AI workflows across enterprise environments. You will develop data pipelines, labeling and evaluation datasets, quality frameworks, and tools to convert raw customer data into actionable context, collaborating with AI researchers and engineers to improve production outcomes in real-world deployments.

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Design and build data systems that power reliable AI workflows across enterprise environments.
•Develop pipelines for collecting, cleaning, transforming, labeling, and evaluating domain-specific data used by AI systems.
•Create data quality frameworks that identify coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes.
•Build tools and workflows that help teams turn raw customer data into usable context for retrieval, evaluation, reasoning, and execution.
•Partner with AI Researchers and AI Engineers to understand how data quality affects system behavior and production outcomes.

Pay and Benefits

Salary: USD 150,000 - 250,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kEquity

Key Requirements

•Experience Building Data Systems for AI, including data pipelines, evaluation datasets, labeling workflows, retrieval corpora, or similar systems that improve model or agent behavior.
•Strong Data Engineering Fundamentals: clean Python and SQL, data modeling, and production-ready pipelines.
•Research-Oriented Builder: ability to investigate data quality, structure, and representation affecting AI system performance.
•AI-Native Working Style: daily use of AI tools to accelerate coding, analysis, debugging, exploration, and workflow automation.
•Ownership Mentality: taking responsibility for whether the data layer enables AI systems to deliver reliable value in production.

Experience:AIData engineeringEnterprise AI

Skills:CommunicationProblem-solvingCollaborationAttention to detailOwnership

Languages:English

Tech Stack:PythonSQL

Company Brief

Distyl

Builds enterprise-grade AI systems and integration services (Distillery) to help Fortune 500 companies become AI-native, delivering measurable operational impact across healthcare, telecom, manufacturing, and finance.

Industry: AI & Machine Learning

Company Size: Medium (51 to 250 employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series B

Headquarters: San Francisco, United States

Founded: 2022

Website LinkedIn Glassdoor

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Distyl

1 month ago

Research Engineers, Data

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Design and build data systems that power reliable AI workflows across enterprise environments.
•Develop pipelines for collecting, cleaning, transforming, labeling, and evaluating domain-specific data used by AI systems.
•Create data quality frameworks that identify coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes.
•Build tools and workflows that help teams turn raw customer data into usable context for retrieval, evaluation, reasoning, and execution.
•Partner with AI Researchers and AI Engineers to understand how data quality affects system behavior and production outcomes.

Pay and Benefits

Salary: USD 150,000 - 250,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kEquity

Key Requirements

•Experience Building Data Systems for AI, including data pipelines, evaluation datasets, labeling workflows, retrieval corpora, or similar systems that improve model or agent behavior.
•Strong Data Engineering Fundamentals: clean Python and SQL, data modeling, and production-ready pipelines.
•Research-Oriented Builder: ability to investigate data quality, structure, and representation affecting AI system performance.
•AI-Native Working Style: daily use of AI tools to accelerate coding, analysis, debugging, exploration, and workflow automation.
•Ownership Mentality: taking responsibility for whether the data layer enables AI systems to deliver reliable value in production.

Experience:AIData engineeringEnterprise AI

Skills:CommunicationProblem-solvingCollaborationAttention to detailOwnership

Languages:English

Tech Stack:PythonSQL

Company Brief

Distyl

Industry: AI & Machine Learning

Company Size: Medium (51 to 250 employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series B

Headquarters: San Francisco, United States

Founded: 2022

Website LinkedIn Glassdoor