Founding Software Engineer [Backend]

We are conquering the $400B Virtual Training and Simulation market by building 3D interactive applications that use next-generation neural rendering techniques and spatial reasoning AI. Backed by a16z Speedrun and Owl Ventures, we are actively fulfilling a government contract and are looking to quickly expand across many heavily regulated industries.

Core Responsibilities

• Design & Implement ML Infrastructure: Architect and build robust ML infrastructure for training, fine-tuning, and serving large language models and neural rendering systems at scale.
• Cloud Architecture: Design and maintain secure, scalable AWS infrastructure following best practices for highly regulated industries.
• ML Ops & Deployment: Implement CI/CD pipelines for ML models, monitoring systems, and automated deployment workflows.
• System Integration: Work closely with the ML team to optimize model serving, reduce latency, and ensure efficient resource utilization.
• Security & Compliance: Implement and maintain security best practices, ensuring compliance with industry standards (SOC 2, NIST 800-171).
• Maintain Code Quality: Follow and contribute to our coding standards, participating in code reviews to maintain a high-quality codebase.
• Performance Optimization: Monitor and optimize system performance, costs, and reliability across our infrastructure.

Requirements

• Strong experience with cloud infrastructure, particularly AWS services (EC2, ECS, S3, Lambda)
• Extensive knowledge of ML Ops tools and practices (MLflow, Weights & Biases, Ray)
• Proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow)
• Experience with containerization and orchestration (Docker, Kubernetes)
• Strong background in system design, distributed systems, and microservices architecture
• Experience with monitoring tools and observability platforms
• Familiarity with infrastructure as code (Terraform, CloudFormation)
• Understanding of security best practices and compliance requirements

Nice to haves

• ML Research Experience: Background in ML research, particularly in computer vision or neural rendering
• DevOps Expertise: Experience with advanced DevOps practices and tools
• Database Expertise: Experience with various database technologies (PostgreSQL, Redis, MongoDB)
• Specific Industry Experience: Defense, mining, energy, or other highly regulated industries
• Full Stack Capabilities: Experience with modern web frameworks (React, Node.js)
• Security Certifications: AWS Security Specialty, CISSP, or similar certifications