Research EngineerOverviewWe are seeking engineers to build platform infrastructure at the intersection of computational science, AI systems, and software engineering.
Role and Responsibilities- Develop a complex agentic system to support emerging superintelligence, with a focus on solving challenges in physics.
- Work across computational science simulation, AI systems, full-stack development, and infrastructure to build the platform enabling AI-driven physics discovery. This role requires fluency in scientific computing concepts, modern software engineering practices, machine learning infrastructure, and production systems design.
- Design production-ready systems, including security considerations.
- Build infrastructure supporting model training, evaluation, and deployment with experiment tracking, versioning, and reproducibility systems
- Implement orchestration for machine learning workloads across cloud infrastructure and develop instrumentation for understanding agent behavior and scaling
- Build production web applications serving research teams and external customers with responsive interfaces, backend services, and APIs
- Create containerized architectures and orchestration systems with CI/CD pipelines, infrastructure as code, GPU scheduling, and compute resource management
What We're Looking ForWe seek candidates with a track record building production systems that technical users adopt, along with strong fundamentals across software engineering, computational methods, and infrastructure. You should have depth in at least two to three relevant technical areas and the ability to work across the full stack from scientific computing to production deployment.
Programming and software engineering:- Python, or similar systems languages with full-stack development using React, TypeScript, Next.js, and modern web frameworks
- Backend services, REST and GraphQL APIs, data systems including PostgreSQL and Redis, and real-time systems
Infrastructure and MLDevOps:- Docker, Kubernetes, container orchestration, cloud platforms including AWS, GCP, or Azure, and infrastructure as code using Terraform
- CI/CD pipelines, monitoring with Prometheus and Grafana, GPU scheduling, and compute resource management
Machine learning infrastructure:- PyTorch, JAX, or similar frameworks with experiment tracking systems such as MLflow or Weights & Biases
- Orchestration frameworks including Ray, Airflow, or Argo, and distributed training infrastructure
Scientific computing and domain knowledge:- High-performance computing environments, physics simulations or domain-specific scientific software
- Building tools at AI labs, machine learning-focused startups, or research organizations
Location and CompensationThis is an in-person role based in Boston or San Francisco or San Jose. We offer competitive compensation including salary, benefits, and meaningful early-stage equity. We evaluate on technical breadth, systems thinking, scientific curiosity, and shipping velocity.
