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Pattern is a fast-growing ecommerce acceleration platform that uses proprietary technology, AI, and machine learning to help global brands optimize and automate growth across 60+ marketplaces including Amazon, Walmart, Target, eBay, Tmall, TikTok Shop, and others. The company processes over 66 trillion data points and serves hundreds of global brands.
As a Senior Machine Learning Engineer, you will own critical components of Pattern's generative content pipeline, focusing on evaluation systems and quality assurance. Your responsibilities include:
- Building and maintaining datasets, rubrics, and automated judges that evaluate the effectiveness of content generation changes
- Converting brand rejection reasons into structured, labeled training data for model improvements
- Quantifying qualitative improvements to generated content
- Setting and defending approval thresholds for generated content in partnership with data science and brand teams
- Building quality gates that catch problematic outputs before brand review, reducing rework cycles
This role sits at the intersection of software engineering and data science, with visibility across one of Pattern's most critical AI systems. You will develop deep expertise in evaluation design, fine-tuning, and production ML—experience that prepares you for senior IC or technical leadership tracks.
In your first 30 days, you'll complete onboarding and contribute to existing regression suites. By 60 days, you'll own a defined slice of the evaluation system end-to-end. By 90 days, you'll independently drive a fine-tuning or retrieval experiment with at least one quality gate live in production.
Pattern values game changers who think innovatively, data fanatics who solve problems through data, partner-obsessed individuals who exceed expectations, and team doers who take initiative and hold themselves accountable.
REQUIREMENTS:
- 3+ years owning production software services end to end
- Strong code and system design experience in any language or stack
- Formal statistics or machine learning training, or equivalent depth built on the job
- Experience engineering systems with non-deterministic outputs, where correctness must 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
