We 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 do

• Train, fine-tune, and evaluate machine learning models on real-world workflow and behavioral data
• Build predictive models for task outcomes, productivity trends, capacity forecasting, and workflow optimization
• Fine-tune large models and foundation models for domain-specific prediction, classification, and embedding tasks
• Design and maintain feature pipelines, training loops, and evaluation frameworks
• Work with engineers and product teams to integrate trained models into production systems
• Monitor model performance and iterate using offline evaluation and live data feedback

Who you are

• Strong foundation in Python and applied machine learning
• Experience training supervised and self-supervised models
• Hands-on experience with model fine-tuning, evaluation, and deployment workflows
• Comfortable working end-to-end from raw data through training to production inference
• Pragmatic, curious, and experimental with a bias toward shipping working models

Bonus points

• Experience fine-tuning large language models or embedding models
• Familiarity with PyTorch, TensorFlow, or similar frameworks
• Experience with time series forecasting, behavioral modeling, or graph-based learning
• Background working with messy, real-world product data

Why join

• Build the learning backbone of Reflow that turns work data into predictions and signals
• Work closely with founders, engineers, and product teams
• Ship real models into production and see them shape how teams work
• Flexible structure, part-time or full-time, with a focus on ownership and iteration speed