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 Senior Machine Learning Engineer, your key responsibilities include building automation and leading-edge architecture around model development, deployment, and serving to optimize time to market and quality of AI/ML applications. Specifically, you will give architectural guidance 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:

• You will work closely with other stakeholders from Product Management, Engineering, and other business stakeholders to create impactful, intelligent features and products. 

• You will collaborate closely with other team members including Data Scientists and Data Engineers and “own” the end to end process.

• You will be given wide authority to develop creative model-based solutions but will also be held to high quality and accountability standards. 

• You will thoroughly and diligently document the model 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. 

• Build and maintain optimal global AI/ML architectures.

• Stay informed of industry trends and enable successful AI/ML solutions by leveraging best practices.

• Participate in and set up Proof of Concepts (POCs) to demonstrate proposed solutions.

• Identify, design, and implement internal process improvements: Automating manual processes, re-designing infrastructure for greater scalability, etc.

• Build infrastructure needed for AI/ML systems, such as 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.

• You will mentor and train more junior team members. 

Let's talk about you:

• 2+ years’ of industry experience

• M.S or PhD in Data Science/Machine Learning or closely related areas such as Computer Science, Operations Research, Applied Statistics, and Biomedical Informatics. 

• Deep grounding in 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. 

• Solid foundation with development of data analytics systems, including data exploration/crawling, feature engineering, model building, performance evaluation, and online deployment of models. 

• Hands-on experience in handling large and distributed datasets on Hadoop, Spark, Hive, etc.  

• Experience with AWS or other cloud-based tools and technologies for data pipelining, model development and deployment.

• Experience building scalable AI/ML systems for continuous training automation, computer vision, natural language processing, or similarly advanced AI/ML problems.

• Experience with one or more of the following AWS (Amazon Web Services) cloud services: SageMaker, ECR/EC2 and Docker, AWS Batch processing, Lambda, Glue, EventBridge, etc.

• Experience with AI/ML operational tools such as Airflow, MLFlow, H2O, etc.

• Experience with relational SQL and NoSQL databases.

• Experience with developing production-grade Python code.