Senior Machine Learning Engineer

LPL Financial Services•San Diego, CA

27d

About The Position

LPL Financial Corp is seeking a highly skilled and experienced Senior Machine Learning Engineer to join our innovative technology team. You will be instrumental in designing, developing, and deploying cutting-edge machine learning solutions that drive business intelligence, optimize operations, and enhance client experiences within the financial services domain.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field. PhD is a plus.
3-5+ years of professional experience in machine learning engineering, with a strong portfolio of successfully deployed ML models in production.
Proficiency in programming languages such as Python (essential) and experience with relevant ML libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
Solid understanding of machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and their machine learning services (e.g., SageMaker, Azure ML, Google AI Platform).
Strong experience with MLOps principles and tools for model deployment, monitoring, and management.
Proficiency in SQL and experience working with large datasets, data warehousing, and ETL processes.
Excellent problem-solving skills, analytical thinking, and attention to detail.
Strong communication and interpersonal skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.

Nice To Haves

PhD is a plus.
Experience with distributed computing frameworks (e.g., Spark) is a plus.
Experience within the financial services industry is highly desirable.

Responsibilities

Lead the full ML lifecycle: Drive end-to-end development of machine learning models, including problem definition, data exploration, training, evaluation, and production deployment.
Collaborate and translate business needs: Work closely with product managers, data scientists, and engineering teams to deliver robust, scalable ML solutions aligned with business requirements.
Design scalable ML pipelines: Implement pipelines with strong data quality, feature engineering, model versioning, and CI/CD practices for seamless deployment.
Ensure performance and reliability: Select appropriate algorithms/frameworks, validate models through A/B testing, and maintain production-grade systems with monitoring and alerting for data drift.
Promote innovation and compliance: Stay current with ML advancements, mentor team members, and uphold best practices in security, data privacy, and regulatory compliance.