Requirements

Strong foundation in ML theory and statistics, including hypothesis testing, probability distributions, regression, classification, and optimization techniques.
Solid engineering fundamentals. You are comfortable writing production-level Python and have a deep understanding of data structures, algorithms, and distributed system design.
Deep proficiency in Python and the modern ML stack, with hands-on experience using libraries like Pandas, NumPy, Scikit-learn, and deep learning frameworks (PyTorch, TensorFlow).
Gradient Debugging: Expertise in PyTorch or JAX, including experience with distributed training (e.g., DDP, FSDP) and debugging complex gradient issues.
Applied Research: Ability to read, implement, and improve upon the latest academic papers (NeurIPS, ICML, CVPR). You understand the math behind libraries and can reproduce results in peer-reviewed papers.
Track record of end-to-end ML delivery, from exploratory data analysis (EDA) and feature engineering to training, validation, and deploying models in a production environment.
Experience with large-scale systems, capable of designing resilient architectures that handle vast datasets and high-throughput inference requests.
Strong engineering mindset, valuing code quality, testing, modularity, and maintainability just as highly as model accuracy.

What you'll be doing

Design, train, and ship production-grade ML models—including deep learning, NLP, and computer vision systems—that solve complex business problems and power core product features.
Conduct deep exploratory research on massive datasets to uncover novel patterns in user behavior and content creation, translating raw data insights into new predictive modeling opportunities.
Apply advanced fine-tuning strategies (e.g., PEFT, LoRA) to adapt state-of-the-art foundation models to specific domain tasks, rigorously experimenting to maximize performance.
Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation, ensuring high data quality and system reliability.
Optimize model performance for latency, throughput, and resource utilization, balancing model complexity with production constraints.
Collaborate cross-functionally with data engineers, product managers, and software engineers to integrate models into user-facing applications.
Champion MLOps excellence by automating deployment workflows, implementing CI/CD for ML, and establishing robust monitoring for model drift and health.
Stay at the forefront of ML research, evaluating novel algorithms and techniques to drive innovation and technical strategy.