Senior Machine Learning Engineer (Generative AI)

Location: Remote/WFH, associated with Durham, North Carolina, United States

Annual base pay: $111,400–$278,500, with potential additional incentive plans, bonuses, and benefits.

Company

hirify.global provides clinical research services, commercial insights, and healthcare intelligence for the life sciences and healthcare industries.

What you will do

• Design and develop machine learning applications across the product portfolio, focusing on generative AI and large language model solutions.
• Provide hands-on technical leadership, define architecture, shape coding standards, and promote reusable software engineering practices.
• Transform machine learning research and human-data expertise into viable prototypes and production-grade algorithms.
• Collaborate with product managers, engineering managers, data scientists, stakeholders, and software engineers to guide technical and business decisions.
• Support multiple Scrum teams and work with external customers as a consultant or solution machine learning engineer.
• Research emerging tools, techniques, and algorithms, mentor junior engineers, and contribute to conference or journal articles.

Requirements

• 5–8 years of experience creating machine learning algorithms for production purposes and a STEM-related bachelor's, master's, or doctoral degree.
• Experience building, testing, measuring, and deploying production machine learning models, including classification, regression, and MLOps processes.
• Experience with LLM engineering, including foundation-model fine-tuning, RAG systems, prompt engineering, and LLM evaluation frameworks.
• Expertise in generative AI applications, multimodal solutions, vector databases, embedding models, context-window optimization, and token management.
• Engineering project leadership using Python, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SciPy, SQL, and Linux or Mac command-line tools.
• Knowledge of agile software development, software testing, continuous integration, DevOps practices, data-product ownership, and mentoring through activities such as pair programming.

Nice to have

• Advanced LLM infrastructure experience, including orchestration frameworks, model quantization, optimization, guardrails, and safety mechanisms.
• Responsible AI experience with hallucination mitigation, generative-model evaluation, and bias detection and mitigation.
• Experience with mixture-of-experts architectures, agent frameworks, model distillation, and efficient fine-tuning.
• Clinical, regulated-data, healthcare, life sciences, biostatistics, or healthcare technology experience.
• Knowledge of AWS, Azure, GCP, Docker, large real-world datasets, CI/CD deployment, UX principles, and product development lifecycles.

Culture & Benefits

• Remote/WFH work arrangement.
• Opportunity to collaborate across multiple software engineering and machine learning teams.
• Potential health and welfare benefits, incentive plans, bonuses, and other compensation depending on the position.
• Opportunities to contribute to research publications and advance healthcare and life sciences products.