Senior Machine Learning Engineer (AI)

TL;DR

Senior Machine Learning Engineer (AI): Designing and deploying advanced Generative AI solutions for a health-tech platform with an accent on information retrieval, summary generation, and GenAI application development. Focus on training and fine-tuning Large Language Models, prompt engineering, and scaling AI capabilities across customer-facing and internal tools.

Location: Remote (US-based due to benefits and work authorization requirements)

Company

hirify.global is a venture-backed Health-Tech company building a next-generation drug acquisition platform to disrupt the Pharmacy Benefit Management (PBM) sector.

What you will do

• Design Generative AI solutions for information retrieval and summary generation for support and operations.
• Partner with Software Engineering teams to build and deploy GenAI applications.
• Ideate and explore opportunities to deploy GenAI technologies for customer-facing experiences.
• Drive the adoption and scalability of Generative AI capabilities within hirify.global.

Requirements

• 5+ years of experience in data science, machine learning, and AI development, with proven success leading AI initiatives.
• MS or PhD in Computer Science, Electrical Engineering, Statistics, Robotics or equivalent fields.
• Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning).
• Proficiency in Python and object-oriented programming.
• Strong experience working with machine learning and natural language processing techniques and tools.
• Strong experience using Generative AI models (GPT, VAE, GANs) and retrieval methods (embeddings).
• Strong experience using key Python packages for data wrangling, machine learning, and deep learning (pandas, sklearn, TensorFlow, torch, transformers, LangChain).
• Experience in Prompt Engineering and few-shot techniques to enhance LLM performance.
• Experience with training and fine-tuning deep learning models, especially LLMs.

Nice to have

• Experience with embedding model training and retrieval method evaluation approaches.
• Experience with LLM architectures, adapters, Mixture of Experts (MoEs) pretraining and fine-tuning techniques.
• Experience with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches.
• Experience with reinforcement learning approaches in the context of fine-tuning LLM outputs.
• Experience with time series analysis and multivariate time series modeling.

Culture & Benefits

• Highly competitive wellness benefits including Medical, Pharmacy, Dental, Vision, Life Insurance, and AD&D Insurance.
• Flexible Spending Benefits and 401(k) Retirement Savings Program.
• Discretionary Paid Time Off and 12 Paid Company Holidays.
• Paid Parental Leave benefits and Employee Assistance Program (EAP).
• Well-stocked kitchen in office locations and professional development and training opportunities.