Role Description As a Machine Learning Operations Engineer II, you are a foundational builder who bridges the gap between complex machine learning development and robust core engineering infrastructure. You partner closely with data scientists, product engineers, and infrastructure teams to deploy, monitor, and scale machine learning solutions in production. You are deeply passionate about automation, system reliability, and scaling pipelines that turn raw code into highly available product features. You balance a technical engineering mindset with an operational focus to maximize the reliability of our production systems. Our team is passionate, empathetic, hard working, and above all else focused on improving the lives of our service professionals (our Pros). Our success is their success. In your day to day, you will: - Implement robust infrastructure for deploying, monitoring, and managing machine learning models in live production environments - Build automated, end-to-end machine learning pipelines focusing on feature engineering, model deployment, and continuous evaluation - Collaborate with data scientists and product engineering teams to operationalize complex models and elevate production readiness - Develop sustainable continuous integration and continuous deployment pipelines to support reproducible model release workflows - Establish comprehensive monitoring, logging, and alerting solutions to ensure peak model performance and system health - Support the continuous optimization of system architecture to improve scalability, uptime, and infrastructure cost efficiency - Evaluate emerging technologies and operational best practices to integrate meaningful upgrades into the team's engineering stack - Document architectural standards, technical processes, and operational procedures to promote cross-team knowledge sharing Qualifications - 3+ years of professional experience in MLOps, Data Engineering, or core infrastructure software roles - Demonstrated proficiency in backend programming languages with a strong emphasis on Python - Hands-on experience deploying, monitoring, and supporting machine learning models within distributed environments - Proven understanding of workflow orchestration systems (i.e. Apache Airflow) - Solid understanding of distributed batch or streaming data tools (i.e. Kafka, Spark) - Experience implementing automated continuous integration and continuous deployment software delivery workflows - Demonstrated ability to leverage AI tools to improve workflows, streamline execution, or enhance outputs - Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent work experience Requirements - Exceptional breadth of interest shown through tangible, self-initiated ventures or deep community involvement; you love trying new things and possess a demonstrated history of successfully pivoting or starting over in life and work - Strong communication and collaboration skills when working across cross-functional engineering pods - A proactive mindset dedicated to automation and eliminating manual operational engineering bottlenecks - High attention to detail when debugging shared infrastructure and pipeline exceptions Benefits - Remote environment: totally built to make you feel that we are all together in one space without leaving your home office! - Self Managed PTO: Beach? Mountains? Camping? Discovering new experiences? You are free to take time out as you need! - Flexible work hours: We believe that you can reach your professional and personal goals working with us and encourage you to have a work life balance! - A culture built on innovation that values big ideas: We are always open to new ideas that will improve the life of our Pros! - MacBook (or PC if you prefer!) + Setup Fee ($500): What is remote work without the right tools? Here at HCP, you can choose your computer and set up your home office!

• Envision, champion, and support the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems • Lead, mentor, challenge and grow enthusiastic, collaborative software engineers and applied scientists across the organization • Raise the AI/ML skillset within the organization. A passion for teaching/mentoring is important.

• Design, implement and own ML metrics and evaluation pipelines spanning offline model evaluation, simulation and on-road performance. • Build and maintain test, regression and hillclimbing suites that gate model and stack releases, including automated triage of regressions to root cause. • Drive model improvement through loss analysis, error mining, and data balancing/curation strategies for training and evaluation sets.

• Deploy and Optimize Machine Learning model architectures across May’s Autonomous Driving training and inference stacks. • Own the model-compilation and deployment pipeline end-to-end. • Establish and defend latency/throughput budgets across the AV stack, including profiling, regression and integrity tests.