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

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 for 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

Flexible structure, part-time or full-time, with a focus on ownership and iteration speed
Build the learning backbone of Reflow that turns work data into predictions and signals
Work closely with founders, engineers, and product teams

ML Engineer

Key skills