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Senior Engineering Manager, MLOps
Senior Engineering Manager leading Quince's MLOps team to build and scale the infrastructure powering production machine learning for its e-commerce business — model training, deployment, serving, and monitoring — on cloud-native tooling like AWS, Kubernetes, Terraform, SageMaker, and Kubeflow, while mentoring engineers and managing compute costs.
Senior Data Scientist, Planning and Forecasting
Senior Data Scientist at Quince builds AI-driven supply chain models for demand forecasting, inventory optimization, and vendor performance using modern techniques like LLMs and agentic feature engineering.
Staff Data Scientist, Planning and Forecasting
Leads AI-driven supply chain planning at a fast-growing DTC retailer, building forecasting, inventory optimization, and vendor performance models from scratch while mentoring junior scientists.
Principal Engineer - Supply Chain Planning
ABOUT QUINCE Quince is a destination for builders, creators, innovators, and operators who want to come together and challenge the status quo. Our mission is simple: make really high quality essentials for really low…
Remote | Software Engineer (GitHub Contributor) — $50–$100/hour
Freelance, fully remote contract role for experienced backend software engineers to support AI training and evaluation: designing coding challenges, building reproducible reinforcement-learning environments and deterministic verifiers, and writing golden reference solutions. Core stack: Python 3, Java, Rust, Go, C++, or TypeScript.
Senior Machine Learning Engineer
Senior Machine Learning Engineer (Grade 7) at the UK Department for Business and Trade's DDaT directorate. Leads design, deployment and MLOps standards for production-grade ML/AI services, providing technical direction, championing responsible AI, and coaching teams. Python-centric work in hybrid, multidisciplinary government teams.
Machine Learning Engineer II
Build and deploy production ML models for adMarketplace's native search advertising platform — relevance, ranking, CTR/CVR prediction, and bidding — owning the full ML lifecycle with MLOps, low-latency serving, and A/B testing. Core stack: Python, SQL, Spark/Hadoop, TensorFlow/PyTorch, cloud infrastructure.