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
Compensation Range: $80K - $160K
Tags & focus areas
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