Design and implement ML solutions to predict performance and optimize GPU-accelerated Spark workloads in enterprise environments.
Develop cutting-edge algorithms and adaptive frameworks to continually enhance Spark performance on GPUs.
Build AI-driven agents and tooling to diagnose system issues and optimize applications.
Collaborate with strategic partners and customers to deploy sophisticated ML solutions across diverse environments.
Maintain up-to-date domain expertise by tracking the latest advances in ML systems and algorithms.
Provide technical mentorship and leadership in data science and ML to a team of engineers.

BS, MS, or PhD, or equivalent experience in Machine Learning, Data Science, Computer Science, or a closely related field.
12+ years designing, implementing, and deploying high-quality ML/DL solutions.
At least 5 years in a technical lead role overseeing ML model development.
2+ years hands-on experience with large-scale data processing platforms such as Apache Spark.
Proven ability to apply modern tooling and best practices across the ML model lifecycle.
Strong Python programming skills and experience with numpy, pandas, scikit-learn, scipy, PyTorch, and TensorFlow.
Extensive experience with advanced ML approaches, including LLM/GenAI, reinforcement learning, and adaptive online ML systems.
Deep expertise in feature engineering, assessing feature importance, and building boosted tree models such as XGBoost.

Principal Machine Learning Engineer, Accelerated Apache Spark

Job Description