Computer Vision 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 Computer Vision Engineer, the models you build won't sit in a notebook waiting for a publication cycle. You'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 CV 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.