Member of Technical Staff, LLM & Production (AI)

Location: San Jose, United States

US base salary: $180,000–$450,000 annually

Company

hirify.global is an artificial intelligence company developing personalized, proactive multimodal intelligence and next-generation hardware interfaces for interacting with the world through speech, text, vision, and persistent memory.

What you will do

• Build and own end-to-end evaluation suites for LLM capabilities, including task specifications, grading logic, and researcher dashboards.
• Operate evaluation harnesses as production services with sandboxed execution, tool calling, multi-turn and agentic tasks, deterministic replay, and caching.
• Own data pipelines from collection and filtering through deduplication, labeling, synthetic data generation, and quality gates.
• Build reinforcement-learning environments, including reward functions, simulators, verifiers, and task generators, and scale them across workers.
• Work with researchers to turn hypotheses into measurable evaluations and environments, then interpret the results.
• Improve research engineering standards through testing, versioned datasets and evaluations, and clear boundaries between training, inference, and evaluation.

Requirements

• 3+ years of software engineering experience, including 1–2 years of hands-on experience with LLM training, evaluation, or data infrastructure.
• Deep Python fluency and practical distributed-systems experience.
• Experience building evaluation infrastructure, including tasks, graders, and supporting systems.
• Working knowledge of supervised fine-tuning and reinforcement learning for language models.
• Production engineering practices including testing, reproducibility, and dependable research tooling.
• Ability to work with ambiguous research ideas and make them measurable.

Nice to have

• Experience with agentic evaluations, tool use, code execution, sandbox environments, and multi-turn tasks.
• Experience with PyTorch, JAX, vLLM, SGLang, or equivalent training and inference stacks.
• Experience running data-labeling operations or collecting human preferences at scale.
• Open-source contributions to evaluation frameworks or public benchmarks.
• Experience with verifiers, code sandboxes, or simulation environments.

Culture & Benefits

• Hands-on individual-contributor role at the intersection of model research and production systems.
• Close collaboration with researchers on multimodal intelligence and AI hardware.
• Full-time employment with a US base salary range of $180,000–$450,000 annually.
• Total compensation may include additional components and benefits depending on the role.