ML Engineer

📋 Description

• Train, optimize, and assess machine learning models utilizing real-world workflow and behavioral data.

• Develop predictive models to forecast task outcomes, analyze productivity trends, anticipate capacity, and enhance workflow efficiency.

• Refine large-scale and foundational models for domain-specific tasks involving prediction, classification, and embedding.

• Create and sustain feature pipelines, training loops, and evaluation frameworks.

• Collaborate with engineers and product teams to seamlessly integrate trained models into production environments.

• Track model performance and make iterative improvements using offline evaluations and feedback from live data.

⛳️ Requirements

• Solid grounding in Python and practical machine learning applications.

• Proven experience in training both supervised and self-supervised models.

• Direct experience with workflows for model fine-tuning, evaluation, and deployment.

• Proficient in managing end-to-end processes from raw data collection to training and production inference.

• Pragmatic, inquisitive, and experimental, with a focus on delivering functional models.

• Additional points for experience in fine-tuning large language models or embedding models.

• Familiarity with frameworks such as PyTorch, TensorFlow, or similar technologies.

• Background in time series forecasting, behavioral modeling, or graph-based learning.

• Experience working with complex, real-world product data.

🏝️ Benefits

• Flexible working arrangements, whether part-time or full-time, emphasizing ownership and rapid iteration.

• Contribute to building the learning backbone of Reflow, transforming work data into actionable predictions and insights.

• Collaborate closely with founders, engineers, and product teams.