Senior Software Engineer, Compute Platform

About AION AION is building an interoperable AI cloud platform by transforming the future of high-performance computing (HPC) through its decentralized AI cloud.
Purpose-built for bare-metal performance, AION democratizes access to compute and provides managed services, aiming to be an end-to-end AI lifecycle platform—taking organizations from data to deployed models using its forward-deployed engineering approach.
AI is transforming every business around the world, and the demand for compute is surging like never before.
AION thrives to be the gateway for dynamic compute workloads by building integration bridges with diverse data centers around the world and re-inventing the compute stack via its state-of-the-art serverless technology.
We stand at the crossroads where enterprises are finding it hard to balance AI adoption with security.
At AION, we take enterprise security and compliance very seriously and are re-thinking every piece of infrastructure from hardware and network packets to API interfaces.
Led by high-pedigree founders with previous exits, AION is well-funded by major VCs with strategic global partnerships.
Headquartered in the US with global presence, the company is building its initial core team in India/London.
Who You Are You're a seasoned engineer who has built and scaled high-performance inference systems for AI/ML workloads.
You've designed distributed systems that handle thousands of requests per second while maintaining sub-second response times and cost efficiency.
You're product-minded—you understand how your technical decisions impact developers using AION's platform and think about the end-to-end user experience.
You're a team player comfortable wearing multiple hats—one day you're building product features, the next you're joining customer calls to understand their deployment challenges, and the day after you're helping with UI/UX, customer success, documentation and product ops.
   Experience with Golang is strongly preferred, and deep exposure to cloud infrastructure (AWS/GCP/Azure), Kubernetes, and distributed systems is essential.
You take ownership of platform-level decisions, think strategically about multi-cloud architecture, and want your work to power AI workloads for thousands of developers globally.
Preferred Attributes: High ownership, self driven and bias for action.
Strong strategic thinking and ability to connect technical decisions to business impact.
Excellent communication and mentoring skills.
Thrives in ambiguity, fast-paced environments, and early-stage startup culture.
Why Join AION?
Work directly with high-pedigree founders shaping technical and product strategy.
Build infrastructure powering the future of AI compute globally.
Significant ownership and impact with equity reflective of your contributions.
Competitive compensation, flexible work options, and wellness benefits Apply Now: If you’re a strong engineer ready to lead architecture and scale next-generation AI infrastructure, we want to hear from you.
Please share: Your resume highlights relevant projects and leadership experience.
Links to products, code, or demos you’ve built.
A brief note on why AION’s mission excites you.
What You'll Do Compute Platform Architecture & Multi-Cloud Integration Design and architect AION's multi-cloud compute platform, building abstraction layers that unify diverse cloud providers (AWS, GCP, Azure, bare-metal data centers) Work directly with cloud providers to expand AION's compute pool—understanding pricing, availability zones, GPU types, and capacity planning Build and maintain the AION managed services Understand and abstract cloud provider differences in storage (block, object, file systems), networking (VPCs, subnets, security groups), and compute resources Design composable platform components that enable forward deployments and promote reusability across AION's infrastructure stack Managed Services Development & Platform Ownership Own end-to-end development of managed services on the compute platform—from design and architecture through execution and production monitoring Build scalable orchestration systems for GPU workloads, container scheduling, and resource allocation Develop robust APIs and control planes for compute lifecycle management (provisioning, scaling, termination) Lead technical discussions on platform reliability, performance optimization, and cost efficiency Infrastructure & Peripheral Services Execute on peripheral platform services including billing systems, usage accounting, observability infrastructure, and compliance tooling Build monitoring and telemetry systems for compute utilization, cost tracking, and performance metrics Establish engineering standards for platform development including code reviews, quality gates, and testing practices Mentor engineers on infrastructure best practices and distributed systems design Technical Skills & Experience If you are meeting some of these requirements and feel comfortable catching up on others, we definitely recommend you to apply: 4+ years of experience building and scaling complex backend systems, cloud infrastructure, or distributed platforms Strong understanding of multi-cloud architectures and experience working with AWS, GCP, or Azure at scale Deep knowledge of cloud abstractions: compute (EC2, GCE, VMs), storage (S3, GCS, EBS), networking (VPCs, load balancers, security groups) Proficiency in Golang strongly preferred; Python, Rust, or other systems languages a plus Experience with Kubernetes, container orchestration, and infrastructure-as-code (Terraform, Pulumi, CloudFormation) Solid understanding of distributed systems principles, consensus algorithms, and state management Experience building APIs, control planes, and platform services for infrastructure management Familiarity with databases (PostgreSQL, Redis, etcd), message queues (Kafka, RabbitMQ), and event-driven architectures Knowledge of GPU orchestration, AI/ML workloads, or HPC systems is highly desirable Experience with observability tools (Prometheus, Grafana, Datadog) and distributed tracing Understanding of cloud billing models, cost optimization strategies, and resource scheduling Bonus / Good to Have Having expertise in one or more of these specializations is highly desired: HPC & Cluster Management: Experience handling large-scale HPC clusters using Kubernetes and Slurm for job scheduling, resource allocation, and workload orchestration Data Engineering: Expertise with data pipelines, ETL systems, and large-scale data processing frameworks Systems-Level Programming: Experience with low-level systems programming such as storage systems, Kubernetes operators, OS-level software development, or daemon services (llm-d, system agents) ML Platform Engineering: Experience productionizing ML pipelines, batch job orchestration, model fine-tuning workflows, and Jupyter notebook orchestration systems Enterprise Deployment: Experience platformizing and packaging software for on-premises deployments or customer VPC installations with emphasis on security, compliance, and operational simplicity