Lead Machine Learning Engineer
About the role
As Lead Machine Learning Engineer on the Digital Intelligence team, you will be collaborating with a high-caliber team of software developers and deep learning experts. You'll build and maintain pipelines for distributed model training on large compute clusters, hyperparameter tuning at scale, and model monitoring, and design and develop ML frameworks and components used for various model implementations.
In this role, you'll apply strong technical judgment to choose the right approaches (including modern LLM-based methods where appropriate), evaluate performance with rigorous metrics, and ensure solutions are reliable, secure, and scalable in real-world environments. You'll also contribute to improving data quality and feedback loops, monitoring models in production, and continuously iterating to reduce agent effort, shorten resolution times, and increase consistency and quality across operational workflows.
Responsibilities
- Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
- Develop and manage pipelines for model promotion and other capabilities related to MDLC.
- Optimize training throughput for large data sources.
- Establish and maintain integrations to platforms and tools related to model monitoring and observability.
- Collaborate with cross-functional teams to integrate new technologies and improve the capabilities of our ML Platform.
- Partner with product, architecture, modeling, and engineering to design robust solutions that power our Digital channels.
Required qualifications, capabilities, and skills
- BS in Computer Science or related Engineering field with 6+ years of experience, or MS degree in Computer Science or related Engineering field with 4+ years experience.
- Solid knowledge and extensive experience in Python and in cloud computing, along with ML frameworks (i.e. pytorch, tensorflow).
- Deep knowledge and passion for data science fundamentals, training and deploying models.
- Experience in monitoring and observability tools to monitor model input/output and features stats.
- Operational experience in big data/ML tools such as Ray, Spark and in training/inference systems such as Ray, vllm/SGLang.
- Solid grounding in engineering fundamentals and enterprise system design.
Preferred qualifications, capabilities, and skills
- Experience with recommendation and personalization systems is a plus.
- CUDA experience is a big plus.
- Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc].
- Good knowledge of data storage solutions and strategies (online and offline).
About the team
The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.
Benefits
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.