About the role

Introducing Cedana

The Problem

AI and HPC  infrastructure suffers from scarcity and high costs, so when failures happen they are costly in terms of time and money. Cluster productivity directly determines research output and revenue. Achieving high utilization and throughput is increasingly challenging due to the complexity of workloads, hardware, and operations. 

Cedana’s Solution

Cedana maximizes AI+HPC cluster utilization and reliability with automated GPU checkpointing infrastructure. We enable transparent and fast migration of GPU workloads across instances, without losing work. Workloads automatically migrate to achieve new levels of reliability and throughput while accelerating time to results. Our system is at the kernel/OS level, requiring no code or config changes, and works seamlessly with Kubernetes, SLURM, and NVIDIA Dynamo. Today, we're deploying into leading inference platforms, neoclouds, enterprise, and research clusters. 

The Team

Cedana's founding team has spent over a decade making computation run fast, productively, and reliably for AI. Our research appears in NeurIPS and CVPR. We published some of the earliest formal methods for guaranteeing convergence in distributed training. At Shopify we've deployed warehouse automation and robot fleets building behavior trees, fleet control planes, and OTA infrastructure that performs reliably over constrained networks. We bring repeat founder experience having built and exited a healthcare AI company.

The Role

What you’ll own

As a Forward Deployed Engineer at Cedana, you’ll lead and own technical engagement from end to end. You’ll engage with customers to understand and deploy in their environments: from production SLURM at a university, bare-metal Kubernetes at an inference provider, hybrid setup at a Fortune 100 Pharma enterprise. You’ll rapidly understand their key pain points, and use Cedana to solve their problems. For each customer you own everything from the OS up: SLURM plugins, Kubernetes operators, node configuration, networking, and observability.

This role will expose you to the cutting edge of AI and HPC infrastructure, working with the world’s leading research and commercial customers to deliver a breakthrough solution.

What You'll Do

• Engineer solutions at client sites: Lead customer integrations. Install, configure, and deploy Cedana into SLURM, Kubernetes, and Dynamo environments.  
• Drive product innovation from the field: Identify technical gaps while embedded with clients, then provide product feedback for new capabilities that become core product features.
• Measure and optimize platform performance: Measure reliability, throughput, and performance using our internal tools. Design and implement policy-based migration automations to optimize reliability, throughput, and performance
• Own critical deployments: Ensure our platform performs reliably for clients' critical operations, debugging issues across the full stack. Debug install issues against unfamiliar customer infrastructure, and escalate to engineering when necessary.
• Improve scalability: Build and own the internal installation playbook so that the second customer in each segment is onboarded faster than the first.
• Respect our customers: Understand how to make their lives easier and minimize their time and overhead.

What we are looking for

• Team management experience. Requires strong project and time management skills, delivering milestones on time, and effective
• 3-10 years of software engineering experience with a track record of configuring and managing SLURM deployments.
• A multi-month enterprise or research deployment you led end-to-end, from scoping through signoff. You write effective status updates to keep your team updated and on schedule.
• Production experience in standing up SLURM in a customer or research environment. You've configured slurmctld, slurmdbd, accounting, cgroup integration, and GPU resource selection.
• Strong Linux fundamentals of systemd, cgroups v2, namespaces, networking, filesystems, firewalls, kernel module loading, PAM session modules. You can read strace and dmesg output and form a hypothesis.
• Experience with Kubernetes operations including operators, CRDs, CNIs, device plugins, and node-level debugging. You've debugged a controller in production even if you haven't written one from scratch.

Bonus if you have

• Experience in an HPC integrator field team 
• Client-facing technical experience working directly with customers.
• Background in national lab user services or university research computing 
• You’ve developed SLURM plug-ins, and understand their architecture and how they fit into the overall platform.  
• Familiarity with CRIU, container runtimes, GPU driver internals, distributed training stacks 
• Hands-on with NVIDIA Dynamo, Determined, Ray, Kueue, KServe, or comparable AI orchestration.
• Contributed to open-source schedulers or job systems (SLURM, Flux, Torque, PBS).
• A passion for debugging a weird cgroup issue at 11pm just as much as writing a clean install playbook the next morning.

Logistics

• Remote, US-based. ~25% travel for customer installs.
• Base $140,000–$180,000 + meaningful early-stage equity.

Benefits

• 100% covered medical, dental, and vision insurance for employees and families
• Unlimited PTO policy
• 401K Plan

Equal Opportunity Employer

Cedana is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status

About the interview

• Initial interview for fit
• Written component to understand background and motivation. Not a coding test.
• Interviews with engineering team.
• References

About Cedana

Cedana is pause/migrate/resume for compute workloads. We're working on building a global, real-time system for compute. This means a paradigm shift in how we allocate resources to things like high performance computing, numerical simulation and training and running machine learning models. We do so by taking a systems-level and deep-tech approach to these problems, working at the Linux Kernel layer and with hardware.