Associate Machine Learning Engineer

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

R&D Product Development

Job Sub Function:

R&D Machine Learning

Job Category:

Scientific/Technology

All Job Posting Locations:

Singapore, Singapore

Job Description:

Johnson & Johnson is seeking an extraordinary and enthusiastic Associate Machine Learning Engineer. This role requires a deep understanding of machine learning algorithms, strong software engineering skills, and the ability to translate business requirements into scalable machine learning solutions.

As a member of the Global Finance Data Science team, you will report to the Principal Machine Learning Engineer. You will be involved in designing, developing and deploying scalable and efficient Machine Learning models and systems on cloud platforms. You will be working closely with Data Scientists to understand technical requirements in Machine Learning projects and devising solutions to meet those needs.

Responsibilities

• Support the end-to-end development of machine learning models, from data collection and preprocessing to model training, evaluation, and deployment.
• Apply machine learning techniques and algorithms, such as deep learning, Generative AI, Agentic AI, natural language processing, reinforcement learning, etc on finance datasets.
• Design and implement scalable machine learning algorithms and systems that can efficiently handle large volumes of data.
• Collaborate with data scientists, data engineers and business stakeholders to understand business requirements, translate them into technical requirements and deliver end-to-end data solutions.
• Develop robust, production-grade code for deploying machine learning models on cloud platforms. Ensure solutions meet reliability, performance, and security standards.
• Implement best practices for model monitoring, performance optimization, and continuous integration/deployment.
• Research and stay updated with the latest developments and trends in machine learning and related fields and establish industry network by participating in internal and external forums, conferences, etc.

Requirements

• At least a Masters degree in Computer Science, Engineering, Business Analytics or an equivalent qualification
• Proficiency in Python, SQL, and Apache Spark; exposure to Databricks and Fabric is a plus.
• Basic familiarity in architecting and implementing LLM-based and agentic AI systems.
• Adherence to software engineering best practices, including writing clean code.
• Basic understanding of CI/CD tools and software, including Jenkins, Git, BitBucket, Spinnaker and Helm.
• Strong team player and you can work effectively in a collaborative, fast paced, high achieving environment.
• Exceptional analytical and problem-solving skills.
• Good communication and presentation skills.

Required Skills:

Preferred Skills:

Analytical Reasoning, Artificial Intelligence (AI), Business Savvy, Cognitive Computing, Data Gathering and Analysis, Data Modeling, Detail-Oriented, Execution Focus, Machine Learning (ML), Natural Language Processing (NLP), Persistence and Tenacity, Project Management, Research and Development, SAP Product Lifecycle Management, Science, Technology, Engineering, and Math (STEM) Application, Scientific Research, Scripting Languages, Technologically Savvy