Applied Research Engineer (AI)

TL;DR

Applied Research Engineer (AI): Designing and implementing RL and post-training methods for frontier AI models with an accent on agentic systems, evaluation frameworks, and distributed infrastructure. Focus on bridging research and customer needs by building robust data pipelines and deploying scalable AI agents in real-world scenarios.

Location: Remote or Hybrid in San Francisco. Relocation support and visa sponsorship available for the US.

Salary: $150k–$300k

Company

hirify.global builds open infrastructure for frontier AI, including evaluation environments and training systems for RL and post-training.

What you will do

• Design and iterate on next-generation AI agents for reasoning-intensive workflows.
• Develop distributed systems and evaluation pipelines to measure and improve agent capabilities.
• Build data capture, versioning, and processing workflows for model alignment.
• Translate customer requirements into technical research and product priorities.
• Prototype and deploy agentic solutions alongside customers to validate performance.
• Maintain and optimize distributed training/inference pipelines for scalability.

Requirements

• Strong background in ML engineering with specific experience in post-training or RL.
• Deep expertise in distributed training and inference frameworks like Ray or vLLM.
• Proficiency in containerized system deployment (Docker, Kubernetes, Terraform).
• Experience with applied data workflows and evaluation frameworks for LLMs.
• Proven track record of research contributions or open-source benchmarking.
• Passion for building practical, agentic AI systems at scale.

Culture & Benefits

• Competitive cash compensation with equity incentives.
• Flexible work arrangements with options for remote or office-based roles.
• Visa sponsorship and relocation support for qualified candidates.
• Professional development budget for conferences and learning.
• Participation in team off-sites and collaborative research environments.