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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Machine Learning Engineer - **Company:** Pattern Inc. - **Location:** Lehi, UT, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, A/B Testing, Artificial Intelligence, Systems Engineering, Machine Learning, Software Engineering, Large Language Models - **Published:** September 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=69cb0d5955d5865e ## About the 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. ## Description * 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., 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., * 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., * 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., * Onsite interview with hiring manager and a panel of department leaders * Professional reference checks * Executive review * Offer ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Fireside Chat: Deep Learning, Deep Impact: Harnessing AI for Language Innovation](https://www.wearedevelopers.com/videos/612-fireside-chat-deep-learning-deep-impact-harnessing-ai-for-language-innovation) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction)