About the roleWe're looking for an engineer to build and operate the core systems that power Phylo's agentic AI platform in production. You'll design and implement the distributed systems, compute environments, and service architecture that allow our AI agents to run reliably at scale - across cloud-native deployments and enterprise environments.
This is an early, foundational role on the team, with real ownership over core infrastructure decisions, a high degree of autonomy, and the ability to meaningfully shape the company's direction from day one.
We are an AI-native engineering team: we use coding agents heavily, move fast, and hold a very high bar for engineering excellence.
What You'll Work On- Design and build production systems that orchestrate agent execution and power AI-driven scientific workloads.
- Build and operate scalable, reliable infrastructure across cloud, hybrid, and on-prem enterprise environments.
- Develop systems for sandboxed execution, secure task isolation, and controlled compute environments. Design and implement security, access control, and compliance foundations suitable for enterprise deployments.
- Partner closely with ML and science teams to translate computational workflows into robust, production-grade distributed systems.
- 3+ years of industry experience in backend, infrastructure, or distributed systems engineering.
- Strong proficiency in at least one programming language (e.g., Python, Go, Rust, or similar).
- Experience designing and operating distributed systems in production.
- Deep hands-on experience with containerization and Kubernetes.
- Experience with infrastructure-as-code tooling (e.g., Terraform, Pulumi, or equivalent).
- Experience operating systems on at least one major cloud provider (AWS, GCP, or Azure).
- Comfort owning systems end-to-end in fast-moving, high-autonomy environments.
Nice to Haves- Experience building enterprise SaaS platform that supports single-tenant, customer hosted deployment patterns.
- Experience in building R&D infrastructure at Pharma/Biotech.
- Experience with job orchestration, task scheduling, or workflow engines.
- Experience with sandboxed or isolated execution frameworks (e.g., gVisor, Kata Containers, Firecracker).
- Familiarity with distributed storage, observability systems, or high-performance compute environments.
