Engineer Jobs in Chevy Chase, MD

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AI Data Engineer

Howard Hughes Medical Institute Chevy Chase, MD

Chevy Chase, MD

Today

$129k - $161k/yr Full time Artificial Intelligence Big Data Bigdata

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Job Description

The AI Data Engineer will build and run the data-engineering foundation that powers HHMI’s AI Accelerator. The work focuses on transforming institutional content into governed, AI-ready datasets, with pipelines and infrastructure designed for reliable retrieval and downstream AI consumption. The role operates under the design authority of the Principal Knowledge & Data Architect, turning established patterns into production-ready systems.

Role focus

Build AI-facing data pipelines that ingest from the landing zone provided by Operations Capabilities, transform data through bronze, silver, and gold layers, and deliver governed, AI-ready outputs for downstream use.
Implement the medallion architecture using design patterns from the K&D Architect.
Create Delta Lake table structures with partitioning, optimization, and schema evolution in mind.

Workflow orchestration and operations

Own the workflow-orchestration framework, including Databricks Workflows and Delta Live Tables.
Apply retry policies, alerting routes, run history, and cost tags to keep pipeline execution observable and dependable.

Governance and access control

Implement governance patterns using Unity Catalog structure.
Apply sensitivity classification, access control, and audit capabilities for AI-facing data assets.

Retrieval-supporting infrastructure

Build and operate embedding pipelines and vector store maintenance.
Support reindexing when models upgrade.
Maintain retrieval evaluation frameworks to support quality and effectiveness.

Partnerships and data contracts

Partner with Operations Capabilities on source-system contracts, including defining schema, cadence, SLA, and quality thresholds for what the AI Fabric consumes at the landing zone.
Own the platform-side of that contract.
Design and operate data quality processes, including data-quality checks, freshness monitoring, drift detection, and alerting that surfaces issues before AI users encounter them.

Reference patterns and team enablement

Contribute to and consume a reference-pattern library that includes reusable pipeline patterns, code templates, and standards in the shared platform layer.
Support AI developer velocity by providing the data engineering needed when AI Developers integrate into product teams and require specific datasets.

Minimum qualifications

At least 4 years of hands-on production data engineering, including designing, building, and operating production data pipelines.
Strong depth with Databricks and Spark technologies, including Delta Lake, medallion architecture, Delta Live Tables, Workflows, Databricks SQL, and Unity Catalog.
Fluency in Python and SQL, including PySpark and ETL patterns, with SQL that runs at scale.
Working familiarity with Git, CI/CD for data pipelines, and infrastructure-as-code using Terraform.
AI-adjacent data engineering experience building the data foundation for AI use cases, including embedding pipelines, vector stores, chunking strategies, and retrieval evaluation.
Practical workflow orchestration experience in Databricks Workflows or Airflow in production, including retry semantics, dependency management, and failure handling.
Data quality and observability experience with tools such as Great Expectations or Databricks data-quality monitors (or equivalent).
Governance discipline that includes Unity Catalog structures and sensitivity classification, with access control and audit considerations built from the first commit.
AWS foundations at the level required for Databricks-on-AWS work, including IAM, S3, and KMS.
Communication ability to work with the K&D Architect, AI Developers, and Operations Capabilities, including explaining data-engineering trade-offs to non-engineers.
Bachelor’s degree or equivalent, plus at least 4 years of hands-on data engineering with meaningful exposure to AI or knowledge-management use cases.

Technologies

Databricks Workflows, Delta Live Tables, Delta Lake, Databricks SQL, Unity Catalog
Python, PySpark, SQL
Git, CI/CD, Terraform
Databricks data-quality monitors, Great Expectations
Embedding pipelines, vector store maintenance, retrieval evaluation frameworks
Databricks-on-AWS, IAM, S3, KMS, Airflow

Hybrid location and reporting

Location: Chevy Chase, MD (hybrid)
Schedule: three days per week in-person at HHMI’s offices in Chevy Chase, Maryland
Reports to: Director of AI Enablement

Compensation and benefits

Salary range: USD 128,816 - 161,021 per year (annual)
Competitive pay
Exceptional health benefits
Retirement plans
Time off
Recognition and wellness programs

Equal opportunity and eligibility

HHMI is an equal opportunity employer and uses E-Verify to confirm identity and employment eligibility of new hires.

Additional practical notes

HHMI cannot sponsor a visa for this position at this time.
This job description outlines principal duties and should not be considered exhaustive; essential duties and responsibilities are treated as essential functions unless otherwise noted.

Not this role

Not a Data Platform Architect: platform design authority for Databricks-on-AWS sits with Platform Architects; this role builds AI-facing data pipelines on that platform.
Not the Knowledge & Data Architect: the K&D Architect owns the knowledge and retrieval design; this role implements against that direction and focuses on hands-on delivery.
Not an AI/ML engineer: AI Developers own model access, orchestration, and application delivery; this role supplies the data layer that AI Developers consume.
Not a warehouse or BI data engineer: traditional analytics warehouses and BI-facing pipelines belong to Operations Capabilities; this role is AI-facing.

Nice to have

Experience with knowledge graphs (Neo4j or comparable), entity resolution, or semantic data models.
Experience with the modern data stack alongside Databricks-native tooling.
Familiarity with LLM-based extraction, chunking, and evaluation frameworks.
Background in research, academic, or mission-driven institutional environments.
Experience with cross-platform data engineering such as Snowflake or BigQuery.

Physical requirements

Ability to remain seated or standing for extended periods and perform reaching, grasping, and keyboard-based dexterity.
Communication using spoken word and ability to see and hear within normal parameters.
Mobility and ability to move materials weighing up to several pounds (for example, a laptop computer or tablet).

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