As one of our Senior Machine Learning Engineers, you will lead the design and development of ML applications across our product portfolio, with a strong focus on generative AI and large language model (LLM) solutions. You will be a hands-on technical leader, providing architecture and shaping coding standards. You will evangelize best practices for software engineering including design, development, and lifecycle maintenance, and will partner with multiple software engineering teams to encourage practices like code reusability, shared libraries, UX-driven design, and a culture of continuous improvement.

You will help guide the transformation of machine learning research domain expertise in the areas of human data into viable prototypes. Your technical leadership will enable our Machine Learning Engineers to build and train new production-grade algorithms that can learn from complex, high-dimensional data to uncover patterns from which machine learning models and applications can be developed. Your ability to research current and emerging industry tools, techniques, and algorithms, and share these findings with colleagues will prove invaluable to your team's success.

You will meet frequently with stakeholders, product managers, engineering managers, data scientists, and other individual contributor engineers to understand a wide array of technical and business impacting variables and distill these into strategic and tactical choices that our Machine Learning Engineering teams will use to develop and improve software products for our customers.

Along with your primary directive of working across the product portfolio to support multiple scrum teams, you will also be expected to work with external customers either as a consultant or as a solution Machine Learning Engineer. There will also be opportunities to prepare and submit conference and journal articles.

Essential Requirements:

• Familiarity with traditional ML algorithms (classification, regression) and MLOps processes
• Experience with building, testing, measuring, and deploying machine learning models in production
• Experience with LLM engineering, including:
• Fine-tuning foundation models (GPT-4, Claude, open-source LLMs)
• Implementing Retrieval-Augmented Generation (RAG) systems
• Prompt engineering and LLM evaluation frameworks
• Expertise in building generative AI applications:
• Development of multimodal AI solutions (text, image)
• Working with vector databases and embedding models
• Context window optimization and token management
• Prior engineering project leadership using relevant skills and technologies:
• Python (Scikit-learn, Tensor Flow, Py Torch, Pandas, Numpy, Scipy)
• SQL, Linux/Mac command-line tools
• Familiarity with agile software development lifecycle (SCRUM, Kanban, etc.)
• Previous experience of owning, maintaining, and enhancing software data products
• Attention to clarity of code, ease of development, and correctness of implementations
• Good knowledge of software development best practices including testing, continuous integration, and DevOps tools
• Experience with mentoring and training junior team members, especially pair programming
• STEM-related degree (Bachelor's, Master's or Doctorate)
• 5-8 years' experience working on creating machine learning algorithms for production purposes

Preferred Requirements:

• Advanced LLM infrastructure experience:
• Orchestration frameworks for complex LLM workflows
• Model quantization and optimization techniques
• Experience implementing model guardrails and safety mechanisms

Responsible AI implementation:

• Hallucination mitigation strategies
• Evaluation frameworks for generative model outputs
• Bias detection and mitigation techniques
• Experience with emerging architectural approaches (mixture of experts, agent frameworks)
• Knowledge of model distillation and efficient fine-tuning methods
• Experience with clinical domain and with regulated data
• Experience with large language models for healthcare applications
• Knowledge of cloud systems such as AWS, Azure, GCP and containerization such as Docker
• Experience working with large, real-world datasets
• Demonstrated in-depth understanding of product development lifecycle
• Demonstrated aptitude for and interest in peer mentorship
• Experience deploying code into production through CI/CD tools
• Knowledge of biostatistics/life sciences/healthcare technology
• Knowledge of UX principles
• Prior management training preferred but not required

IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com

IQVIA is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other status protected by applicable law. https://jobs.iqvia.com/eoe

IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.

The potential base pay range for this role, when annualized, is $108,000.00 - $270,000.00. The actual base pay offered may vary based on a number of factors including job-related qualifications such as knowledge, skills, education, and experience; location; and/or schedule (full or part-time). Dependent on the position offered, incentive plans, bonuses, and/or other forms of compensation may be offered, in addition to a range of health and welfare and/or other benefits.