Sr. AI/ML Engineer- Life Sciences

abbott; United States; Remote; USD 78,000 - 156,000; Full time

Job summary

Abbott is seeking a Sr. AI/ML Engineer to guide architecture, roadmaps, and hands‑on development for a life sciences AI platform. The role spans technical leadership across Bioinformatics and Data Science, with collaboration to Clinical Affairs and Medical Operations in cancer diagnostics.

You will help shape AI strategy, evaluate new tech, and build reusable, validated solutions. You will design platform services, integrate agent frameworks, and mentor junior engineers while ensuring governance

Qualifications

• Advanced degree with quantitative focus; 3+ years in AI/ML fields.
• Experience with neural networks, deep learning, reinforcement learning.
• Experience with NLP, image processing or CV is preferred.
• Experience with generative AI and LLMs; transformer concepts.

Responsibilities

• Guide architecture, roadmap, and evolution of the life sciences generative AI platform.
• Design shared platform services and reusable components.
• Integrate agent frameworks with enterprise data sources and systems.
• Translate business needs into scalable AI solutions.
• Drive platform adoption with evaluation criteria for quality and value.
• Apply Agile practices to mature concepts into reusable solutions.
• Mentor junior teammates and lead in cross-functional meetings.
• Communicate complex information to technical and non-technical stakeholders.
• Support Quality Management System policies and regular attendance.
• Travel up to 5% and work across multiple locations as needed.

Skills

Python programming

AI/ML algorithms

NLP/Computer vision basics

Transformer architectures

RAG and retrieval concepts

Agile methods

Communication to both technical and A/

Education

PhD in Statistics/Computational Biology/CS

Master's in Statistics/Computational Biology/CS with 4+ years experience

Tools

TensorFlow

PyTorch

SKLearn

Open-source AI models

Job description

JOB DESCRIPTION

Position Overview

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.

Essential Duties

• 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.
• 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.

Minimum Qualifications

• 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.

Preferred Qualifications

• 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

The base pay for this position is

$78,000.00 – $156,000.00

In specific locations, the pay range may vary from the range posted.

JOB FAMILY

Product Development

DIVISION

ONCO Cancer Diagnostics

LOCATION

United States of America : Remote

WORK SHIFT

Standard

TRAVEL

Yes, 5 % of the Time

MEDICAL SURVEILLANCE

Not Applicable

SIGNIFICANT WORK ACTIVITIES

Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day), Keyboard use (greater or equal to 50% of the workday)

Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.

EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdf

EEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf