Research Engineers, Data

Distyl AI, San Francisco +1

What you will do

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
Develop synthetic data, annotation, and feedback-loop strategies to improve system performance in areas where real-world data is sparse or noisy

All jobs

Research Engineers, Data

Distyl AI, San Francisco +1

Posted pay

$150K–$250K

a year

Compared with similar jobs

This base pay is below the median for AI research jobs in the Bay Area with no level in the job title.

Median: $235K a year
Other jobs in the group: 528

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location: San Francisco +1
Work type: Hybrid
Level: Not given
Skills in the job text: Python
SQL
Posted: Jul 7, 2026
Last check: 1 h ago
Job type: Research

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Distyl AI

Open tech jobs: 17
With posted pay: 94%
Median posted base: $150K–$250K

About Distyl AI: funding, hiring and pay

Distyl AI

About Distyl AI: funding, hiring and pay

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What you will do

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
Develop synthetic data, annotation, and feedback-loop strategies to improve system performance in areas where real-world data is sparse or noisy

What you need

What we are looking for

At Distyl, Research Engineers build the bridge between frontier AI research and production systems that deliver real business value. This role is for engineers who are excited to investigate how AI systems should be designed, rapidly prototype new ideas, and turn promising concepts into reliable systems that work inside real customer environments.

Research Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment. They design and implement compound AI systems, run experiments to understand system behavior, build evaluation frameworks, and collaborate closely with AI Researchers, AI Engineers, and customer stakeholders. Their work is not limited to demos or isolated prototypes: they help turn new techniques into robust systems that can be measured, operated, and improved in production.

Who you are

Experience Building Data Systems for AI: You have built data pipelines, evaluation datasets, labeling workflows, retrieval corpora, or similar systems that improve model or agent behavior
Strong Data Engineering Fundamentals: You write clean Python and SQL, understand data modeling and pipeline reliability, and can build systems that are maintainable under production constraints
Research-Oriented Builder: You are comfortable investigating how data quality, structure, and representation affect AI system performance
AI-Native Working Style: You use AI tools daily to accelerate coding, analysis, debugging, exploration, and workflow automation
Comfort with Ambiguous Data: You can reason through messy enterprise datasets, incomplete documentation, conflicting business definitions, and changing requirements
Bias Towards Measurement: You prefer to make data quality and system behavior observable through concrete metrics, evaluations, and experiments
Customer Environment Readiness: You can work directly with customer teams to understand their data, ask precise questions, and explain tradeoffs clearly
Ownership Mentality: You take responsibility for whether the data layer enables the AI system to deliver reliable value in production

Research Engineers, Data

What you will do

Research Engineers, Data

What you will do

What you need

What we are looking for

Who you are

Pay and benefits

About Distyl AI

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