Lead Machine Learning Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Work collaboratively with cross-functional teams including Product Managers, Data Scientists, Data Engineers, Software Engineers, and Business units to develop data and Machine Learning products.

• Assume responsibility for objectives and key results within your workstream, and partner with your manager to own technical solutions.

• Design and construct resilient systems for training, deploying, running inference, and monitoring Machine Learning and AI systems at scale.

• Advocate for code quality, reusability, scalability, maintainability, and security, while contributing to strategic architectural decisions.

• Establish processes and tools to guarantee data quality, enforce data governance policies, and uphold engineering best practices.

• Integrate Machine Learning and AI systems into production applications.

• Innovate using new methodologies, keeping up-to-date with the latest research and technologies within the ML engineering community.

⛳️ Requirements

• A completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or a related quantitative discipline.

• Over 7 years of experience as an engineer focused on building Machine Learning systems.

• At least 2 years of technical leadership experience in delivering machine learning solutions alongside engineers, scientists, and business stakeholders.

• Proficient programming skills in Python and a solid understanding of core computer science principles.

• Familiarity with machine learning and AI frameworks and libraries such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc.

• Capability to design, train, and assess machine learning and AI models while adhering to best practices, including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, and dimensionality reduction.

• Experience with MLOps practices, encompassing automated model deployment, model performance monitoring, and data drift detection.

• Proficient in building batch and streaming pipelines utilizing complex SQL, PySpark, Pandas, and similar frameworks.

• Experience with data warehouses (e.g., dimensional modeling), data lakes/Lakehouses, and other data architectures.

• Ability to orchestrate complex workflows and data pipelines using Airflow or similar tools.

• Competence in load testing deployed models at scale to identify performance bottlenecks.

• Familiarity with Git, CI/CD pipelines, Docker, and Kubernetes.

• Experience architecting solutions on AWS or equivalent public cloud platforms.

• Proficient in developing data APIs, Microservices, and event-driven systems for ML system integration.

• Knowledge of Large Language Models (LLMs) and other generative AI modalities, along with their application in production environments.

• Experience in evaluating and implementing new data tools to enhance the machine learning stack.

• Strong interpersonal and verbal communication skills.

• Proven technical leadership experience with the ability to mentor and guide others.

🏝️ Benefits

• Paid time off (vacation, holidays, sick leave).

• Medical, dental, and vision insurance.

• 401(k) plan available to eligible employees.

• Long-term incentive programs.