Research Scientist, Applied AI

Human Capitalists; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Human Capitalists is seeking a senior AI researcher to build the core agent behavior for a revenue-focused AI system, guiding from hypothesis to production. You will lead hands-on development of agent harnesses, tool use, and multi-step reasoning within a fast-moving team.

You will design and evaluate experiments, contribute to publications when possible, and collaborate with a US-based leadership group across time zones to push research toward production-ready solutions.

Qualifications

• MS or PhD in Computer Science, Mathematics, Statistics, or another quantitative discipline.
• 5+ years of experience in applied AI research or applied science, at a senior level.
• Hands-on experience building and improving agentic systems and agent harnesses, including tool use, multi-step reasoning, and context management.
• Hands-on experience with LLMs or large-scale deep learning systems, including training, fine-tuning, and RLHF.
• Understanding why models behave the way they do, not just how to use them.
• Experience designing eval datasets or frameworks, esp. for tasks without clean ground truth.
• Proven ability to scope and drive ambiguous research with minimal guidance.
• Publications at top-tier venues preferred.
• Comfortable working with a US-based leadership team across time zones.

Skills

Agentic systems

LLM experience

Large-scale DL

Reasoning and context management

Research leadership

Education

MS or PhD in CS/Math/Stats or quantitative field

Job description

Build the core agent behaviour behind a revenue-focused AI agent, from the agent harness and evals to fine-tuning and reinforcement learning, and take hard research problems from hypothesis to production.

• MS or PhD in Computer Science, Mathematics, Statistics, or another quantitative discipline.
• 5+ years of experience in applied AI research or applied science, at a senior level.
• Hands-on experience building and improving agentic systems and agent harnesses, including tool use, multi-step reasoning, and context management.
• Hands-on experience with LLMs or large-scale deep learning systems, including training, fine-tuning, and RLHF.
• A clear understanding of why models behave the way they do, not only how to use them.
• Experience designing eval datasets or frameworks, especially for tasks without clean ground truth.
• Proven ability to scope and drive ambiguous research with minimal guidance.
• Publications at top-tier peer-reviewed conferences or journals are preferred.
• Comfortable working with a US-based leadership team across time zones.