Applied AI Researcher

This is an opportunity to join a seed stage venture backed startup currently in stealth, with a repeat founder (previous unicorn). We are seeking a scientist that loves to experiment and innovate to work with a small group of incredibly talented engineers working on cutting edge AI infrastructure that will reshape how companies scale inference, from the application layer to the data center.

This will be an incredibly fulfilling and creative role, offering autonomy and self direction to work at speed on genuinely novel research problems, with a strong need for you to help contribute and drive the direction of our technology.

This is a full-time hybrid role for an Applied AI Researcher, located in McLean, VA, with an aim to meet in person a few days a week but primarily work remotely.

As an applied scientist, you will be responsible for designing, developing and models and techniques that power our infrastructure's intelligence layer. You'll bridge the gap between research and production, turning SOTA research into ML systems that run at scale.

We need a scientist who thrives in ambiguity and loves shipping models that solve real problems.

You have significant autonomy to shape our research direction while working on:

• AI-driven predictive models for capacity planning, autoscaling, and resource optimization

• Self-healing systems powered by ML that detect and resolve infrastructure failures autonomously

• Novel scheduling algorithms using machine learning for GPU orchestration and workload distribution

• Agentic AI systems for infrastructure management and optimization

• ML models that learn from production data to continuously improve system performance

• Scientist who loves to build and deploy AI/ML models in production

• Strong ML engineering skills with experience taking models from research to production

• Experience with distributed training, and model optimization

• Hands-on experience with predictive modeling, time series forecasting, and anomaly detection

• (Ideally) experience with reinforcement learning, multi-agent systems, or control theory

• Strong adoption of AI tools for research, experimentation, and rapid prototyping

• Deep understanding of current AI/ML research landscape and ability to implement papers quickly

• Interest in exploring SOTA in self-healing systems, scheduling algorithms, and agentic infrastructure

• Ability to work across the stack from research papers to production systems

If you want to push the boundaries of what's possible in AI-driven infrastructure and build systems that think for themselves, we would love to talk.