ML Engineer

CreatorIQ is the operating system for creator-led growth trusted by more than 1,300 global brands and agencies. We’re on a mission to make businesses more human, and humans more impactful. We operate by our values — be intentional, pursue excellence every day, embrace the journey together, and be a good human — every day. CreatorIQ has earned the title of best companies to work for in multiple programs, including Built In LA and NY.

It’s been named a Fastest-Growing Company in North America on the Deloitte Technology Fast 500™ for four years, was named a leader in IDC Market Scape:
Worldwide Influencer Marketing Platforms for Large Enterprises in 2025, was named a Leader by The Forrester New Wave™:
Influencer Marketing Solutions, and has been consistently recognized by G2 as a Leader, and is rated 5 stars on Influencer Marketing Hub. We operate in a flexible work model that combines both in-person and remote work to boost collaboration, enhance innovation, and adapt to individual work styles.

We're seeking passionate, innovative minds to join our journey. Be a part of our dynamic team and let's transform the industry together!

As a MLE you'll join the Product Innovations team and work across the full applied ML stack - deploying models, building the evaluation systems that tell us whether they actually work, and making the data and infrastructure decisions that turn experimental data science into cost-efficient products. You'll partner closely with our Data Science and Engineering teams on our vector embeddings ecosystem, ground truth pipelines, model evaluation, and the pre/post-processing decisions that determine product quality.

This is a production focused role, with some research opportunities. You'll be the engineer who makes sure our ML systems - both traditional NLP and embedding models and our LLM-powered features - work reliably at scale (millions of records per day), are continuously evaluated against ground truth, and improve over time.

• Deploy and monitor ML systems in production, from classical NLP and embedding models to LLM-powered features - where "production" means millions of records per day
• Own the evaluation stack - golden datasets, "model-as-a-judge" frameworks, inter-annotator agreement, and regression tests that gate releases
• Build and maintain our vector embeddings ecosystem and the retrieval, classification, and similarity patterns that sit on top of it
• Partner with Data Science on annotation workflows, PII scrubbing, and ground-truth pipelines
• Improve our MLOps foundations - versioning, observability, drift detection - so the rest of the team can ship faster
• Translate fuzzy product problems into measurable AI features with clear success criteria

• 4–7 years of professional software or ML engineering experience, including 2+ years shipping ML systems to production
• Strong Python; comfort with the modern data/ML stack
• Hands-on experience deploying and monitoring models in at least one major cloud (AWS or GCP); willingness to learn the other
• Production experience with NLP or ML systems - classification, NER, embeddings, ranking, similarity, or LLM-powered features (most candidates have done some mix of traditional ML and LLM work; we care that you’ve shipped, not which camp you came up in)
• Practical experience with evaluation for ML or LLM systems - golden datasets, model-as-a-judge, IAA, precision/recall, or equivalent. You don’t need to have built one from scratch, but you should know why they matter and how to improve them
• Collaborative communicator - you work well alongside data scientists and engineers, and can clearly explain ideas, requirements, and tradeoffs to non-technical stakeholders

• Experience with vector databases or retrieval systems at scale
• Experience with managed ML services on AWS (Sage Maker) and/or GCP (Vertex AI)
• Annotation workflow experience (Label Studio, Scale AI, or similar) and a point of view on inter-annotator agreement
• Familiarity with PII scrubbing patterns and privacy-by-design data handling
• Open-source contributions, blog posts, or talks on LLM/embedding production work