Applied AI Researcher (Agent Systems & Evaluation)

Location: Mountain View, California, United States

Salary: $193,930–$352,290 annual base pay, plus annual performance bonus, equity, and benefits.

Company

hirify.global is a self-driving technology company developing scalable autonomous-driving systems that combine AI with automotive-grade hardware.

What you will do

• Establish the evaluation foundation for hirify.global's production agent fleet, including data sources, task suites, noise floors, and statistical acceptance standards.
• Convert real engineering workflows into closed-loop measurements that run on every system change.
• Research frontier model developments and turn relevant findings into experiments against real production workloads.
• Investigate test-time scaling, sampling and search strategies, verifier-guided selection, routing, reasoning budgets, and model escalation.
• Build automated hill-climbing workflows for prompts, context strategies, tools, routing, reasoning budgets, and model selection.
• Run supervised fine-tuning and reinforcement-learning experiments on open-weight vision-language models using proprietary autonomous-driving data.

Requirements

• Graduate degree in computer science, machine learning, statistics, or a related field, or equivalent research experience.
• Deep understanding of LLM pretraining, post-training, inference, and model mechanisms.
• Experience across the full evaluation pipeline: sourcing data, constructing evaluation loops, and automating improvement.
• Strong experimental design skills, including understanding variance, statistical power, and production experimentation constraints.
• Hands-on post-training experience with supervised fine-tuning and reinforcement learning, plus data curation and evaluation.
• Strong Python skills, production-systems experience, and direct experience building, evaluating, or studying LLM agent systems.

Nice to have

• Published or applied work in agent evaluation, reasoning, test-time compute, reinforcement learning, or verification.
• Experience with multimodal or vision-language models and large-scale data curation.
• Experience with A/B testing, causal inference from observational data, and offline-to-online correlation.
• Experience building evaluation harnesses, task suites, or LLM-as-judge systems.
• Familiarity with autonomous systems, safety cases, or verification-gated deployment.

Culture & Benefits

• Small startup-style team operating within an established self-driving company.
• Direct collaboration with engineering leadership and the CEO, with ownership of technical decisions.
• Access to production agent systems, compute, proprietary driving data, and a labeling workforce.
• Annual performance bonus, equity, and a competitive benefits package.
• Commitment to inclusion, diversity, and psychological safety.