Machine Learning Engineer

At The ReWork Group, we partner with high-growth startups and forward-thinking companies to build the future. Our client is building a platform where AI doesn't just analyze data; it reasons across it, forecasts what's coming, and acts on its own. As a ML Engineer , the models you build won't sit in a notebook waiting for a publication cycle. They'll run in production, at enterprise scale, making real calls on messy, heterogeneous, real-world data. This is a hybrid role by design. Some weeks you'll be deep in computer vision training and deploying object detection and segmentation models on SAR and electro-optical satellite imagery. Other weeks you'll be building agentic systems: designing tool-calling workflows, orchestrating LLM-driven analysis pipelines, and building the evaluation infrastructure that keeps them reliable. The work varies significantly project to project, and the right candidate sees that as a feature, not a bug. This is applied ML at its most impactful. What You'll Do: Design, build, and deploy ML models for demand forecasting, time-series prediction, consumer sentiment analysis, and anomaly detection — at enterprise scale. Develop and iterate on our agentic AI architecture — systems that reason across heterogeneous data sources and take autonomous action. Own robust ML pipelines end to end — data preprocessing, feature engineering, model training, evaluation, and production deployment. Architect and sharpen our production graph RAG system — one of their core technical differentiators. Build RAG systems and LLM integrations that power natural-language interfaces and autonomous workflows. Partner with backend engineers to make models genuinely production-grade — tuned for latency, reliability, and scale. Own model performance in the wild — monitoring, retraining, and continuous improvement once it's live. Stay at the frontier of AI research and pull the innovations that actually matter into the platform. Who You Are: Senior enough to think deeply about architecture and tradeoffs — but you still have boundless energy for implementation. You'd rather build it than describe it. High agency, low ego. You move without waiting for permission, and you don't need the credit. A great communicator who can make complex systems legible to the people who depend on them. 5+ years of experience in applied machine learning and AI, with models deployed and running in production environments M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or related field (or equivalent practical experience — what you've built matters more than the degree) Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn) Experience with NLP, LLMs, and RAG architectures.