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 doTrain, 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
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
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
We 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.
Skills Required
- 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
