Senior ML Engineer: Evaluation & Generative Content

Pattern Inc.; Lehi, Northern (UT, KY); Hybrid; USD 140,000 - 180,000; Full time

Benefits offered by this job

Unlimited PTO

Paid Holidays

Onsite Fitness Center

Company Paid Life Insurance

Casual Dress Code

Competitive Pay

Health Vision and Dental Insurance

401(k) match

Job summary

Pattern Inc. is seeking a Senior Machine Learning Engineer to own evaluation systems for its generative content platform. You will build datasets, rubrics, and automated judges to assess model updates across hundreds of thousands of listings.

You will convert rejection reasons into labeled data and set thresholds in collaboration with data and brand teams. The role focuses on non-deterministic outputs, requiring careful measurement of content quality and robust quality gates before brand review.

Qualifications

• 3+ years owning production software services end to end.
• Experience engineering systems with non-deterministic outputs and measurable correctness.
• Strong coding and system design capabilities across languages or stacks.

Responsibilities

• Build and maintain evaluation datasets, rubrics, and automated judges.
• Convert brand rejection reasons into structured training data.
• Quantify qualitative improvements in generated content.
• Define approval thresholds for generated content with data teams.
• Create quality gates to catch problematic outputs before brand review.

Skills

System design

Production ownership

ML training

Non-deterministic outputs

Education

Formal statistics or ML training

Tools

LoRA/PEFT

LLM experience

A/B testing infra

Job description

Pattern Inc. is seeking a Senior Machine Learning Engineer to own evaluation systems for its generative content platform. You will build datasets, rubrics, and automated judges to assess model updates across hundreds of thousands of listings.

You will convert rejection reasons into labeled data and set thresholds in collaboration with data and brand teams. The role focuses on non-deterministic outputs, requiring careful measurement of content quality and robust quality gates before brand review.