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. Machine Learning Engineer, Applied AI As an MLE, you'll join our 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. What You’ll Do 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. What You’ve Done 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. Practical experience with evaluation for ML or LLM systems—golden datasets, model‑as‑a‑judge, IAA, precision/recall, or equivalent. Collaborative communicator—you work well alongside data scientists and engineers, and can clearly explain ideas, requirements, and tradeoffs to non‑technical stakeholders. Bonus Experience with vector databases or retrieval systems at scale. Experience with managed ML services on AWS (SageMaker) and/or GCP (Vertex AI). Annotation workflow experience (Label Studio, Scale AI, or similar). Familiarity with PII scrubbing patterns and privacy‑by‑design data handling. Open‑source contributions, blog posts, or talks on LLM/embedding production work. What You Will Get From Us People: Work with talented, collaborative, and friendly people. Guidance: Utilize our learning platform for training and tools. Surprise meal stipends: Enjoy meals from the comfort of home. Work/life harmony: 15 days vacation, floating and set holidays, wellness allowance, and paid parental leave. Whole Health Package: Medical, dental, vision, life, disability insurance, and more. Savings: A 401k (USA) plan to help you plan ahead. Work from home stipend: Assist in setting up a home office. Compensation Range: $132K - $165K. #J-18808-Ljbffr