Staff Software Engineer, ML Platform

Location: Remote US, collaboration primarily during EST hours

Cake is on a mission to make cutting-edge AI accessible to enterprise teams.

Enterprises want to move faster with AI, but are constrained by infrastructure complexity, high operating costs, and the governance required to run AI systems safely at scale. Cake removes those barriers, enabling teams to deploy and operate AI/ML platforms 10x faster and 10x cheaper than traditional approaches—without sacrificing reliability or control. Cake runs inside the customer’s own VPC, giving enterprises full ownership of their data, security, and operations.

Cake solves the full infrastructure problem across 4 layers: compute infrastructure management, open-source ML components, common integrations, and pre-built project components. Built-in security, monitoring, and governance ensure clean ownership, enforce guardrails, and provide a dependable path from experimentation to production at scale. 

Backed by top investors, Cake is seeing strong adoption and is positioned for rapid growth in the next 12 months. Our culture emphasizes ownership, clear communication, and collaboration, with a high bar for operational excellence and production-ready systems. 

As a Staff Software Engineer, you will play a critical leadership role in building and operating the infrastructure that powers Cake’s AI platform. This is a high-ownership role for an engineer who thrives at the intersection of distributed systems, cloud infrastructure, and developer experience.

You’ll design and operate the ML platform foundations that both internal teams and customers rely on, owning systems end-to-end from architecture to production. You’ll work closely with customers to translate real-world ML use cases into reliable, scalable platform capabilities.

This role is ideal for someone who wants to be a technical owner, not just an implementer, someone who cares deeply about system quality, operational excellence, and clear communication.

You will:

• →Build Enterprise-Scale Infrastructure
• →Leverage infrastructure-as-code to manage complex cloud environments supporting critical ML and AI initiatives.
• →Design Kubernetes-native systems, including controllers/operators where appropriate.
• →Improve platform networking, security, and observability
• →Sustain Platform Health and Performance
• →Own critical systems in production, including reliability, scalability, security, and cost efficiency.
• →Identify and proactively address technical debt, operational risk, and platform bottlenecks.
• →“Learn by doing” — Quickly ramp up to a complex tech stack (Terraform, Kubernetes, Istio, Crossplane, Go, TypeScript)
• →Enable Teams and Customers to Move Faster 
• →Create abstractions and tooling that make it easier for teams and customers to deploy, run, and scale AI/ML workloads. 
• →Collaborate directly with customers to understand their ML infrastructure challenges and translate them into platform improvements.
• →Balance speed and rigor—shipping quickly while maintaining a high bar for quality and safety.
• →Lead Through Influence 
• →Act as a technical leader and mentor across the engineering organization.
• →Write clear documentation and design proposals that align stakeholders and drive decisions.
• →Partner closely with product and leadership to shape platform direction and priorities.

• Core Experience 
• 10+ years of engineering experience, with significant time spent on infrastructure, platform, or distributed systems.
• Deep hands-on experience with Kubernetes in production environments.
• Strong cloud experience across AWS, GCP, and/or Azure.
• Proven track record of building and operating secure, scalable MLOps platforms.
• Technical Strength
• Deep understanding of infrastructure-as-code (e.g., Terraform, Pulumi, CDK).
• Strong programming skills in at least one backend language (Go preferred; TypeScript also welcome).
• Experience diagnosing and debugging complex production issues.
• Familiarity with modern CI/CD, test-driven development, and DevSecOps practices.
• Bonus: experience building Kubernetes operators and/or working with service meshes (e.g., Istio).
• Ownership & Communication 
• Comfortable owning large, ambiguous problems from inception to production.
• Excellent communicator, able to clearly explain complex systems to both technical and non-technical audiences.
• Experience working directly with customers and incorporating feedback into technical decisions.
• Ability to operate autonomously while keeping stakeholders informed and aligned.
• Mindset 
• Customer-first and product-oriented.
• Curious, adaptable, and eager to learn new systems and domains.
• Collaborative, respectful, and willing to lean into hard conversations.
• Energized by fast-paced environments and meaningful responsibility.