Sr. AI/ML Engineer - Life Science

Abbott•San Diego, CA

10d•Remote

About The Position

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries. Position Overview: The Sr. Machine Learning Engineer is largely self-driven, working in collaboration with others on one or more projects which are extremely complex in scope. This role is responsible for successfully leading the planning and execution of projects. Working in a team setting, this position applies deep knowledge of advanced artificial intelligence and machine learning algorithms and models to solve complex problems involving biological, genomic, clinical and healthcare data within a setting of advanced cancer screening and precision oncology. This position provides technical leadership at every step of the solution development process, from ideation, design, implementation and deployment. The Lead Machine Learning Engineer will help craft the strategic vision for the application of cutting-edge AI methodologies at Exact Sciences and work as a partner with biostatisticians, bioinformatics scientists, and data scientists to further our goal of helping eradicate cancer by preventing it, detecting it earlier, and guiding personalized treatment.

Requirements

Ph.D in Statistics, Computational Biology, Computer Science, or related quantitative field as outlined in the essential duties, or Master’s Degree in Statistics, Computational Biology, Computer Science, or related quantitative field as outlined in the essential duties plus 4 years of relevant experience.
5+ years of experience in statistics, computational biology, applied mathematics, or related quantitative field as outlined in the essential duties.
5+ years of experience with artificial intelligence and machine learning algorithms.
Demonstrated experience with and mastery of advanced AI concepts, such as artificial neural networks, deep learning, and reinforcement learning.
Demonstrated experience with and mastery of artificial intelligence and machine learning techniques within one or more of the following fields: natural language processing, image processing and computer vision, image and pattern recognition.
Demonstrated experience with and mastery of large language models for generative AI and associated concepts, such as transformer architecture and retrieval augmented generation.
Strong programming ability with demonstrated experience in Python and one or more associated machine learning frameworks, such as TensorFlow, PyTorch, or SKLearn.
Knowledge and experience working with open-source AI models.
Excellent communication skills and the ability to explain complex topics to audiences of varied backgrounds.
Demonstrated ability to perform the essential duties of the position with or without accommodation.
Authorization to work in the United States without sponsorship.

Nice To Haves

8+ years of life sciences industry experience working with biological data.
8+ years of industry experience in molecular diagnostics, preferably cancer diagnostics.
Expertise in data mining approaches within healthcare settings generating insight from routinely collected healthcare data.
Basic knowledge of ML-Ops and processes for managing the versioning and deployment of machine learning models.
Scientific understanding of cancer biology

Responsibilities

Lead development of perspectives on technology “affordance”: what do these advances in AI technology allow us to do (more efficiently and / or more effectively) that we couldn’t do before
Work with stakeholders to identify, refine, prioritize and formalize requirements for ML- and AI-based solutions to business problems with a focus on bioinformatics, biostatistics and data engineering applications.
Collaborate with other ML/AI engineers and relevant partners to provide technical and strategic leadership for the design of software solutions that utilize state-of-the art artificial intelligence and machine learning techniques.
Work with other AI and ML engineers and software engineers to lead the implementation and deployment of ML and AI solutions.
Set up Agile framework focused on delivering iterative, “rapid” solutions that demonstrate the promise of selected AI technologies.
Provide leadership in the development of an AI strategy at Exact Sciences that empowers the team to leverage both existing AI and ML tools and those developed in-house to accelerate and enhance bioinformatics, biostatistics and data science processes and outcomes.
Lead internal initiatives to build training resources, develop knowledge bases, and educate on advanced AI and ML technologies and best practices for leveraging them.
Lead “AI evangelism” for biostatistics, bioinformatics and data science at Exact Sciences.
Assist team to solve complex problems using sophisticated analytical thought to exercise judgement and identify innovative solution
Provide mentorship and coaching to more junior level team members.
Act as resource and subject matter expert in core team and/or cross-functional meetings.
Contribute to the intellectual property portfolio by implementing innovative and creative ideas to solve problems or improve on current methods.
Exercise discretion and judgment within broadly defined practices and policies in selecting methods and techniques and evaluation criteria for obtaining and interpreting results.
Ability to effectively communicate, influence decision-making, and explain difficult, sensitive, and/or highly complex information to audiences of peers.
Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
Support and comply with the company’s Quality Management System policies and procedures.
Maintain regular and reliable attendance.
Ability to act with an inclusion mindset and model these behaviors for the organization.
Ability to work designated schedule.
Ability to work on a mobile device, tablet, or in front of a computer screen and/or perform typing for approximately 85% of a typical working day.
Ability to travel 5% of working time away from work location, may include overnight/weekend travel.