Sr. Machine Learning Engineer

We need a Senior Machine Learning Engineer to own evaluation systems and model-side quality improvements for Pattern's generative content platform, the system that produces AI-generated titles, bullets, A+ modules, and imagery across hundreds of thousands of listings. You will build reliable evaluation systems and improvements across non-deterministic systems and you will own the model-side work that acts on what those measurements find.

This is a full-time role and will work a hybrid schedule based in Lehi, Utah.

What is a day in the life of a Senior Machine Learning Engineer?

• Build and maintain the datasets, rubrics, and automated judges that evaluate the efficacy of changes to the content engine.
• Convert brand rejection reasons into structured, labeled training data that feeds the next round of model improvements.
• Find ways to quantify qualitative improvements to generated content.
• Decide and defend approval thresholds for generated content in partnership with data science and brand teams.
• Build quality gates that catch problematic outputs before they reach brand review, reducing rework cycles across the pipeline.

What will I need to thrive in this role?

• Strong code and system design experience in any language or stack.
• 3+ years owning production software services end to end.
• Formal statistics or machine learning training, or a defensible equivalent depth built on the job.
• Experience engineering systems with non-deterministic outputs, where correctness has to be measured rather than assumed.
• Nice to have: Fine-tuning experience (LoRA/PEFT), hands-on LLM or generative media production work, evaluation-system ownership, multimodal evaluation, e-commerce domain knowledge, human-labeling operations, or A/B testing infrastructure.

What is my potential for career growth?

At Pattern, we prioritize internal mobility and professional development. This role sits at the intersection of software engineering and data science on one of Pattern's most visible AI systems, building deep expertise in evaluation design, fine-tuning, and production ML - experience that prepares you for senior IC or technical leadership tracks across Pattern's broader AI and generative content initiatives.

What does success look like in the first 30, 60, 90 days?

• 30 Days: Complete onboarding, get up to speed on the generative content pipeline and existing evaluation datasets and rubrics, and make initial contributions to an existing regression suite.
• 60 Days: Own a defined slice of the evaluation system end to end (for example, judges and thresholds for a specific content type), and begin converting brand rejection reasons into labeled training data.
• 90 Days: Independently drive a fine-tuning or retrieval experiment from hypothesis to validated result, with at least one quality gate live in production catching issues before brand review.

We want individuals who are:

• Game Changers- A game changer is someone who looks at problems with an open mind and shares new ideas with team members, regularly reassesses existing plans and attaches a realistic timeline to goals, makes profitable, productive, and innovative contributions, and actively pursues improvements to Pattern's processes and outcomes.
• Data Fanatics- A data fanatic is someone who recognizes problems and seeks to understand them through data, draws unbiased conclusions based on data that lead to actionable solutions, and continues to track the effects of the solutions using data.
• Partner Obsessed- An individual who is partner obsessed clearly explains the status of projects to partners and relies on constructive feedback, actively listens to partner's expectations, and delivers results that exceed them, prioritizes the needs of your partners, and takes the time to create a personable experience for those interacting with Pattern.
• Team of Doers- Someone who is a part of a team of doers uplifts team members and recognizes their specific contributions, takes initiative to help in any circumstance, actively contributes to supporting improvements, and holds themselves accountable to the team as well as to partners.

What is the hiring process?

• Initial phone interview with Pattern's talent acquisition team
• Video technical interview
• Onsite interview with hiring manager and a panel of department leaders
• Professional reference checks
• Executive review
• Offer

How can I stand out as an applicant?

• Discuss professional accomplishments with specific data to quantify examples
• Provide insights on how you can add value and be the best addition to the team
• Focus on mentioning how you would be partner obsessed at Pattern
• Share experience on any side projects related to data and analytics