AI Engineer

About the role

MeeBoss is focused on creating the infrastructure necessary for a future driven by simulations. Our platform integrates various simulation engines - covering physics, economics, social dynamics, and organizational structures - into cohesive, cross-domain forecasts that no single-domain tool can achieve. We model complex interdependencies, such as how a drought might lead to commodity price fluctuations, shifts in consumer sentiment, and changes in policy, all validated against historical data. Our team is small and committed, ensuring that every member has a real stake in the work we do. We prioritize curiosity, thoroughness, and the ambition to tackle challenging projects. If you want your contributions to influence how organizations comprehend and navigate a complex world, we invite you to join us.

Key facts

What you'll do

• Take ownership of a production simulation platform by designing essential reusable abstractions, objects, interfaces, and evaluation frameworks.
• Develop and manage infrastructure across multiple domains.
• Enhance multi-agent systems for improved performance and cost efficiency.
• Collaborate closely with researchers and customer-facing teams.
• Create analysis and output layers that transform raw simulation data into actionable insights.

Requirements

• Proficient in using "claude code" in high-stakes production environments.
• Extensive knowledge of machine learning and artificial intelligence, with significant experience in large language models.
• Proven track record of conducting comprehensive ML experiments from hypothesis formulation to deployment.
• Minimum of 3 years of practical experience in developing ML/AI systems in research or applied contexts.
• Familiarity with the entire model lifecycle, including data handling, training, evaluation, and production.
• Experience in delivering ML-driven features in collaboration with engineering and product teams.
• A strong desire to explore the potential of simulation technologies.

Nice to have

• Publications in leading machine learning conferences such as NeurIPS, ICML, or ICLR.
• Experience in building or fine-tuning multi-agent systems.
• Knowledge of probabilistic modeling, Bayesian inference, or causal reasoning.
• Practical experience with agent-based modeling, discrete-event simulation, Monte Carlo methods, or similar systems.
• Understanding of hybrid architectures that combine large language models with traditional statistical or machine learning techniques.
• Expertise in quantitative fields such as quantitative finance or computational social science.
• A research-oriented mindset, comfortable with reading academic papers, prototyping alongside researchers, and contributing to published research.

Practical notes

This position is based in Seattle, and in-person attendance is required. Candidates should either reside in the Seattle metro area or be willing to relocate. We offer complimentary on-site housing as part of our benefits package.