Sr. AI/ML Engineer- Life Sciences

Abbott

8d•$78,000 - $156,000•Remote

About The Position

The Sr. AI/ML Engineer guides the architecture, technical roadmap, and hands-on implementation of the life sciences AI platform providing the foundations for agentic solutions across the science and medical offices. This role helps shape the organization’s AI strategy and evaluates emerging state-of-the-art technologies for practical implementation within the platform. This role serves both technical consumers in the Bioinformatics, Biostatistics and Data Science group, and non-technical consumers across Clinical Affairs, Regulatory Affairs, and Medical Operations within a setting of advanced cancer screening and precision oncology. The work combines pragmatic experimentation with a clear path beyond prototypes, producing solutions that are evaluated, validated, governed, supported, and reusable. This role defines the engineering standards, controls, and validation framework under which generative AI solutions are developed and published, and provides technical leadership through mentorship and architecture review.

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 experience in lieu of a Ph.D.
3+ years of experience in statistics, computational biology, applied mathematics, or related quantitative field as outlined in the essential duties.
3+ years of experience with artificial intelligence and machine learning algorithms.
Demonstrated knowledge and experience with advanced AI concepts, such as artificial neural networks, deep learning, and reinforcement learning.
Demonstrated knowledge and experience using 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 knowledge and experience with 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 of and experience working with open-source AI models.
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

2+ years of life sciences industry experience working with biological data.
2+ 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

Guide the architecture, technical roadmap, and strategic evolution of the life sciences generative AI platform, including its runtime, gateway, memory, identity, observability, and evaluation capabilities, to accelerate and improve scientific and medical processes and outcomes.
Design and implement shared platform services, reusable components, and supported solution patterns that generalize recurring needs and reduce reliance on function-specific point solutions.
Integrate agent frameworks and interoperability protocols with enterprise data sources, document repositories, and scientific systems using secure, supported platform patterns.
Partner with stakeholders and AI/ML engineers across science and medical functions to translate operational workflows, SOPs, and business needs into scalable, supported AI solutions.
Drive platform adoption by defining and applying evaluation criteria that demonstrate solution quality, reliability, efficiency, and business value.
Apply Agile practices to iteratively develop, evaluate, and mature promising concepts into validated, reusable solutions.
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.
Communicate difficult, sensitive, and complex information clearly to technical and non-technical stakeholders.
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 and availability during the designated work schedule.
Act with an inclusion mindset and model these behaviors for the organization.