Senior Software Engineer, AI Systems

About the Role

We are hiring a Senior Software Engineer, AI Systems who blends strong, production-focused programming skills with genuine enthusiasm for AI. This is not a role for someone who wishes AI would “go away”; we want engineers who lean into AI as a collaborator and tool for discovering new ideas in computer and networked systems. The goal is to transform computer systems design and optimization with AI by building world-class software.

You will help design, build, and ship the first versions of our product, working closely with the founders and engineering team to translate deep research into robust, scalable production systems

• Design, implement, and deploy core, high-performance software components of our AI-driven optimization stack.
• Collaborate with the founders to translate research insights into real, usable software systems and APIs.
• Own critical pieces of the platform's distributed architecture: routing, scheduling, autoscaling, inference engine integrations, and high-volume data pipelines.
• Build prototypes quickly, validate their performance, and iterate based on user and customer feedback.
• Help establish technical architecture, robust coding standards, and best practices for a high-velocity engineering culture.
• Set up and maintain production development workflows, cloud infrastructure, monitoring tooling, and CI/CD deployment pipelines.
• Evaluate third-party tools/services and make key build vs. buy decisions to optimize development velocity and system cost.
• Document design and development work and help establish the foundation for future engineering hires.

Because this field is new, we don’t expect years of experience. We’re looking for engineers with great systems intuition, intellectual curiosity, and a strong desire to embrace AI-driven engineering.

Strong signals include:

• Deep expertise in Python and modern software development best practices.
• Proven experience designing and building large-scale, high-performance distributed systems.
• Strong fundamentals in PyTorch or similar ML frameworks, and experience integrating models into a production environment.
• Hands-on experience with deployment technologies like Docker, Kubernetes, and cloud infrastructure (AWS, GCP, or Azure).
• Experience with AI inference, distributed serving, and optimizing MLOps pipelines.
• Comfort with performance analysis and profiling of complex systems.

• Hands-on with vLLM, Triton inference server, Ray Serve, or other inference/serving frameworks.
• Experience with low-level systems performance engineering, including GPU optimization, kernel design, or CUDA programming.
• PhD or equivalent experience (PhD or equivalent + ~5 years is a good sweet spot).
• Dislike for rigid bureaucratic engineering processes, but appreciation for disciplined engineering rigor.