Senior AI Data & Evaluation Engineer

Marathon TS; Washington (District of Columbia); On-site; USD 140,000 - 210,000; Full time

Job summary

Marathon TS in Washington, DC area seeks a senior engineer to turn loosely defined customer needs into production-grade data, evaluation, and ML-assisted workflows for advanced AI systems. You will own the full lifecycle of AI data pipelines, building tooling that makes human-in-the-loop data generation and review faster, more reliable, and higher quality.

The role sits at the intersection of data science, data engineering, AI engineering, and delivery operations.

Qualifications

• 5+ years in data science, data engineering, ML engineering or similar roles.
• Strong hands-on experience with Python and SQL data tooling.
• Familiar with ML/LLM solutions and production workflows.
• Track record turning ambiguous requirements into technical specs and delivery.

Responsibilities

• Build and deploy evaluators and design quality measurement systems that validate outputs.
• Generate synthetic datasets by developing new pipelines or adapting existing ones to speed up engagements.
• Package and deliver production-grade datasets with standardized formatting, documentation and QA.
• Build custom applications and tooling for non-standard customer requirements.
• Define production specifications and workflows and secure technical alignment with customer teams for go-live.
• Act as the technical point of contact for delivery leads, resolving blockers and supporting discussions.
• Maintain consistency of specifications across customer and internal teams.
• Document best practices and spot automation opportunities to improve delivery.
• Run model benchmarking and maintain solution leaderboards to show capability.
• Improve evaluation frameworks, assets and delivery processes to raise speed, quality and scale.

Skills

Python

SQL

ML engineering

Data pipelines

APIs

Tools

APIs

Model benchmarking

Job description

Marathon TS in Washington, DC area seeks a senior engineer to turn loosely defined customer needs into production-grade data, evaluation, and ML-assisted workflows for advanced AI systems. You will own the full lifecycle of AI data pipelines, building tooling that makes human-in-the-loop data generation and review faster, more reliable, and higher quality.

The role sits at the intersection of data science, data engineering, AI engineering, and delivery operations.