Research Engineer / Scientist – Reinforcement Learning (RL, AI)

TL;DR

Research Engineer/Scientist (Reinforcement Learning): Advancing RL capabilities for decision-making in critical industries like healthcare, manufacturing, and energy with an accent on real-world deployment and optimization. Focus on developing RL methods for complex planning tasks, building experimental infrastructure, and transitioning research to production platforms.

New York City; Boston

Company

hirify.global transforms critical institutions with applied AI through forward-deployed expertise, in-house Mosaic toolkit for agentic workflows, and partnerships with Anthropic, McKinsey, AWS, and General Catalyst.

What you will do

• Identify real-world challenges tractable for RL-guided decision making.
• Develop RL methods for complex tasks in planning, decision-making, or optimization.
• Build and maintain experimental infrastructure including simulation environments, data pipelines, training, and evaluation frameworks.
• Conduct large-scale in-the-wild evaluations driving significant business value.
• Partner with applied AI engineers to integrate research into Mosaic platform features.
• Communicate research outcomes to technical and non-technical stakeholders.

Requirements

• MS/PhD in Computer Science, ML, or related field, or equivalent experience.
• Track record of effective RL work.
• Motivated by impact in critical industries including healthcare, supply chains, energy, and finance.
• Experience performing rigorous RL experimentation.
• Strong ownership mindset.
• Belief in AI's transformative potential for critical industries.

Nice to have

• High-performance large-scale distributed systems.
• Large-scale LLM or RL training.
• Strong Python programming skills.
• Implementing LLM post-training algorithms.
• Experience with vLLM/SGLang, Ray, Kubernetes (or AWS EKS).
• Distributed checkpointing, multi-node/multi-GPU training, custom KV-caching.
• Asynchronous training/inference with VeRL, ROLL, SkyRL, AReal, or CleanRL.

Culture & Benefits

• Dream bigger: Tackle ambitious problems with optimism and responsibility.
• Heart in the game: Commit fully to meaningful work without hour monitoring.
• Win for the customer: Focus on delivering outcomes over outputs.
• Make the call: Empower high-agency decisions with open communication.
• Intensity with kindness: Excel in execution, feedback, and prioritization while building trust through kindness.