Lead Applied Scientist – AgentForce (AI)

TL;DR

Lead Applied Scientist – AgentForce (AI): Driving hands-on LLM research and model development for production-grade AI agents with an accent on full model development lifecycle, reinforcement learning/continuous learning pipelines, and production readiness. Focus on translating research prototypes into scalable, reliable, and safe models while leading technical POC work and mentoring scientists and engineers.

Location: Palo Alto, California

Company

hirify.global builds production-grade AI agents powered by core large language models.

What you will do

• Own and execute hands-on work across the full model development lifecycle: data preparation, training, fine-tuning, evaluation, iteration, and deployment readiness.
• Lead end-to-end research initiatives on LLM training, fine-tuning, alignment, and optimization for production use cases.
• Design and implement reinforcement learning and continuous learning pipelines (e.g., RLHF, RLAIF, offline/online feedback loops).
• Run rigorous experimentation, ablation studies, and failure analysis to improve model quality.
• Serve as technical POC for complex AgentForce AI projects and align research, engineering, product, and platform teams.
• Mentor junior scientists and engineers through technical guidance and code-level reviews; contribute via publications, talks, and collaborations.

Requirements

• PhD in Computer Science, Machine Learning, AI, or a related field.
• Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent industry research impact.
• Demonstrated hands-on experience owning the full model development lifecycle (not limited to research or design).
• Deep expertise in large-scale LLM training and fine-tuning.
• Strong background in reinforcement learning, preference learning, or human-in-the-loop learning.
• Advanced Python skills and deep experience with PyTorch, TensorFlow, or similar deep learning frameworks.

Nice to have

• Experience deploying and iterating on models in production high-availability systems.
• Background in enterprise AI, agentic systems, or LLM platforms at scale.
• Familiarity with trust, safety, or governance frameworks for AI systems.
• Experience with large-scale distributed compute environments (multi-GPU / multi-node training).

Culture & Benefits

• Work on mission-critical LLM systems at massive scale with end-to-end ownership from research to production impact.
• Emphasis on scientific rigor, reproducibility, and ownership.
• Collaboration across research, engineering, product, and platform teams.
• Opportunity to contribute to external research presence through publications and talks.

Hiring process

• Interviews focused on research/model development depth, hands-on execution, and technical leadership/mentorship experience.
• Discussion of alignment between long-term research/modeling strategy and production requirements.