Principal Machine Learning Engineer
Leads engineering initiatives to build scalable ML platform infrastructure, including unified embeddings, feature pipelines, and continuous learning systems. Requires 7+ years in applied ML, Python/ML frameworks expertise, and Master's/PhD in quantitative field.
About the job
Responsibilities
- Scale ML innovation by building tools, infrastructure, and workflows that dramatically improve the speed and reliability of model development.
- Work backward from modeling needs to design systems that directly unlock gains in accuracy, efficiency, and scientific productivity.
- Explore new algorithms and methodologies for our machine learning models and develop tooling to support them.
- Improve the entire ML lifecycle—from data readiness and feature development through training, evaluation, serving, and monitoring.
- Automate and standardize operational workflows, enabling scientists to focus on high-leverage modeling and analysis rather than manual pipelines.
- Define the roadmap for our next generation ML Platform, balancing near-term impact with long-term architectural scalability.
- Collaborate cross-functionally with Data Engineering, ML Platform, Pricing, and other teams to build reliable, end-to-end ML systems.
Minimum Qualifications
- 7+ years of hands-on experience in applied machine learning, with strong exposure to production-scale modeling efforts.
- Demonstrated expertise in end-to-end model development: data prep, feature engineering, training, evaluation, and deployment.
- Experience working in high-scale, ML-driven product environments—especially in fintech, pricing, or risk modeling.
- Proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost).
- Ability to work autonomously and lead technical direction in ambiguous, high-impact domains.
- Experience collaborating with cross-functional teams including ML scientists, engineers, and product partners.
- Ability to bridge engineering and science teams, and influence technical strategy across disciplines.
- Numerically-savvy and smart with ability to operate at a fast pace.
- Master’s degree or PhD in a quantitative discipline, or equivalent additional professional experience.
Preferred Qualifications
- Practical experience optimizing ML workflows using CUDA/GPU acceleration.
- Background in feature store design, embedding architecture, or synthetic data generation for model training.
- Proven track record of improving model accuracy in production environments with measurable business outcomes.
- Familiarity with modern experimentation frameworks, hyperparameter tuning tools, and automated model selection techniques.
Skills
Python, PyTorch, TensorFlow, scikit-learn, Xgboost, Machine Learning, Feature Engineering, Kubernetes, CUDA, Feature Store
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