Member of Technical Staff, Computer-Use Agents (AI)

Location: San Jose, United States

Salary: $180,000–$400,000 annually

Company

hirify.global is an artificial intelligence company developing proactive, multimodal intelligence and next-generation hardware for interaction through speech, text, vision, and persistent memory.

What you will do

• Build and scale interactive computer-use environments, task generators, realistic applications, stateful workflows, verifiable success checks, and synthetic trajectories for supervised fine-tuning and reinforcement learning.
• Design benchmarks and evaluation harnesses for task success, reliability, efficiency, and safety on long-horizon tasks, and convert model failures into data, environment, or training improvements.
• Develop and scale supervised fine-tuning, reinforcement learning including PPO and GRPO, reward modeling, graders, and verifiers for capable and efficient agents.
• Identify, prototype, and rigorously test novel algorithms and approaches using strong baselines and ablations.
• Build filtering, curation, difficulty calibration, and curriculum methods to improve training data quality.
• Build and scale agent training and inference infrastructure while improving throughput, latency, reproducibility, observability, cost, and production model performance in collaboration with harness, product, pretraining, and infrastructure teams.

Requirements

• Strong software engineering and prototyping skills, with a track record of building high-quality, reliable systems used by researchers and engineers.
• Ability to turn open-ended problems into clear hypotheses, design rigorous experiments, and reason from noisy results.
• Hands-on experience training, fine-tuning, or evaluating large language or multimodal models.
• Deep expertise in at least one of data and environment engineering, agentic evaluations, reinforcement-learning post-training, or training and inference infrastructure for agents.
• Comfort with ambiguity, independent investigation of promising ideas, and ownership of load-bearing work.
• Clear technical communication and ability to collaborate across research, infrastructure, and product.

Nice to have

• Experience training computer-use, coding, web, browser, or other tool-using agents.
• Experience with reinforcement learning in long-horizon or sparse-reward settings, reward models, LLM judges, or verifiers.
• Experience with large-scale distributed training or rollout systems, including Ray, sandboxed virtual machines or containers, or browser automation.
• Publications or open-source work in agents, post-training, evaluation, or multimodal learning.
• Experience translating product signals into model improvements for a shipped AI product.

Culture & Benefits

• High-ownership role on a small, fast-moving, high-performance team.
• Collaboration across computer-use agents, harness, product, pretraining, and infrastructure teams.
• Full-time employment with additional compensation components and benefits potentially included in the package.