Sr. Machine Learning Engineer

Let’s talk about the team:

The Data Science and AI/ML team collaborates across the organization to identify, develop and deliver Artificial Intelligence (AI) and Machine Learning (ML) powered software solutions that improve patient outcomes, delight our partners and customers, and improve the way we do business in an “AI First” fashion. On any given day, the team could be working on building sophisticated models to delivering personalized recommendations to patients to improve their sleep, identify optimal equipment and settings using our unparalleled store of billion+ nights of sleep data, proactively identify other health risk factors, or help optimize complex, global supply chain operations. 

As a Sr. Machine Learning Engineer, your key responsibilities include contributing to and leading development of ML architecture and operations around model development, deployment, and serving to optimize time to market and quality of AI/ML applications. Specifically, you will work on projects within AI operations team to ensure our global AI/ML systems are production-grade, scalable and use the latest state of the art technology and methodology. You will help define and ensure best coding practices within the team of excellent and engaged engineers. You will be given creative freedom and opportunities to work on advanced AI/ML problems, such as reinforcement learning and a self-serve AI/ML platform. You will do hands-on code development, mentor junior team members, and interact with business stakeholders. 

Let's talk about the role:

• Work closely with stakeholders from Product Management, Engineering, and other business stakeholders to create impactful, intelligent AI/ML features and products. 
• Collaborate closely with other team members including Data Scientists and Data Engineers and “own” the end-to-end process, train and mentor junior team members
• Build and maintain optimal global AI/ML architectures and provide production support to live AI/ML products
• Thoroughly document the model & ML system design, experiments, tests, validations, and live metrics and outcomes, typically on Confluence. You may be asked to write documents for use in the preparation of intellectual property and technical publications. 
• Stay informed of industry trends and enable successful AI/ML solutions by leveraging best practices.
• Identify, design, and implement internal process improvements: Automating manual processes, re-designing infrastructure for greater scalability, etc.
• Build capabilities needed for production-level AI/ML systems, such as data processing, model inference, automated re-training etc.
• Work with stakeholders including the Executive, Product, Data and Design teams to help with AI/ML-related technical issues and support their AI/ML infrastructure needs.
• Actively handle escalated incidents to resolution and suggest solutions to limit future exposure.
• Participate in Code Review and process improvement.

Let's talk about you:

• 4+ years’ industry experience in Machine Learning Engineering, Data Science, or Data Engineering
• M.S or PhD in Data Science/Machine Learning or closely related areas such as Computer Science, Operations Research, Applied Statistics, and Biomedical Informatics. 
• Solid foundation with development of data analytics systems, including data exploration/crawling, feature engineering, model building, performance evaluation, and online deployment of models. 
• Experience building scalable AI/ML systems for continuous training automation, computer vision, natural language processing, recommender system or similarly advanced AI/ML problems.
• Hands-on experience in handling large and distributed datasets on Spark, Hive, relational SQL and NoSQL databases.
• Experience with developing production-grade Python code.
• Experience with AWS or other cloud-based tools and technologies for data pipelining, model development, deployment, monitoring, and MLOps tools including SageMaker, Airflow, Kubeflow, etc
• Rigorous academic or experiential knowledge of the mathematical essentials for Data Science, including key concepts in probability and statistics, optimization, time series analysis, linear algebra and discrete math. Sampling and estimation, Bayesian analysis, hypothesis testing, uncertainty estimation, stochastic methods, and graphical methods are particularly important to know. 
• Experience with machine learning techniques including regression methods (linear, logistic, lasso, support vector, etc.), classification (tree-based models such as XGBoost and Random Forest, Neural Networks, Deep Learning – CNN, RNN, LSTM, etc.), as well as knowledge of clustering and unsupervised learning, time series forecasting and optimization methods. 

Joining us is more than saying “yes” to making the world a healthier place. It’s discovering a career that’s challenging, supportive and inspiring. Where a culture driven by excellence helps you not only meet your goals, but also create new ones. We focus on creating a diverse and inclusive culture, encouraging individual expression in the workplace and thrive on the innovative ideas this generates. If this sounds like the workplace for you, apply now! We commit to respond to every applicant.