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Shared by 
2 months ago
Requirements and responsibilities
OverviewWe are building Reflow, a workforce and workflow intelligence platform that helps teams understand and improve how work gets done. At the core of Reflow is a growing set of machine learning models that learn from real work patterns to predict outcomes, surface insights, and power intelligent automation.What you will doTrain, fine-tune, and evaluate machine learning models on real-world workflow and behavioral dataBuild predictive models for task outcomes, productivity trends, capacity forecasting, and workflow optimizationFine-tune large models and foundation models for domain-specific prediction, classification, and embedding tasksDesign and maintain feature pipelines, training loops, and evaluation frameworksWork with engineers and product teams to integrate trained models into production systemsMonitor model performance and iterate using offline evaluation and live data feedbackWho you areStrong foundation in Python and applied machine learningExperience training supervised and self-supervised modelsHands-on experience with model fine-tuning, evaluation, and deployment workflowsComfortable working end-to-end from raw data through training to production inferencePragmatic, curious, and experimental with a bias toward shipping working modelsBonus pointsExperience fine-tuning large language models or embedding modelsFamiliarity with PyTorch, TensorFlow, or similar frameworksExperience with time series forecasting, behavioral modeling, or graph-based learningBackground working with messy, real-world product dataWhy joinBuild the learning backbone of Reflow that turns work data into predictions and signalsWork closely with founders, engineers, and product teamsShip real models into production and see them shape how teams workFlexible structure, part-time or full-time, with a focus on ownership and iteration speedCompensationWe offer competitive pay based on the market and where you’re located. The salary ranges in our job postings are intentionally wide because they need to cover both U.S. and international candidates. Our final offer will depend on things like your experience, skill set, and location.
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