Research Scientist - Reinforcement Learning (AI)

Location: On-site in New York City or Boston, United States

Company

hirify.global applies frontier AI to transform critical institutions in healthcare, manufacturing, energy, supply chains, and finance through embedded engineering, product, and research expertise.

What you will do

• Identify real-world challenges that can be addressed through reinforcement-learning-based decision-making.
• Develop reinforcement learning methods for planning, decision-making, and optimization.
• Build and maintain simulation environments, data pipelines, and training and evaluation frameworks.
• Conduct large-scale in-the-wild evaluations with measurable business impact.
• Work with applied AI engineers to transition research into robust features for the Mosaic platform.
• Communicate research outcomes and practical implications to technical and non-technical stakeholders.

Requirements

• MS or PhD in Computer Science, Machine Learning, or a related field, or equivalent experience.
• Track record of effective reinforcement learning work and rigorous experimentation.
• Strong programming skills, especially in Python.
• Experience or interest in applying AI to critical industries such as healthcare, supply chains, energy, and finance.
• Ownership mindset and motivation to deliver measurable customer impact.

Nice to have

• Experience with high-performance, large-scale distributed systems or large-scale LLM and reinforcement learning training.
• Experience implementing LLM post-training algorithms.
• Experience with vLLM, SGLang, Ray, Kubernetes, or AWS EKS.
• Experience with distributed checkpointing, multi-node and multi-GPU training, custom KV-caching, and asynchronous training or inference.
• Experience with VeRL, ROLL, SkyRL, AReaL, or CleanRL.

Culture & Benefits

• Work is focused on ambitious applied AI problems with direct impact on critical industries.
• Customer outcomes are prioritized over outputs, with engineers expected to deliver measurable results.
• Teams value empowered decision-making, direct communication, and collaboration across research, product, and engineering.
• The culture combines high standards, candid feedback, urgency, and kindness.
• The role is not structured around monitoring fixed working hours and requires sustained commitment to consequential work.