AI Inference Core - SW Engineer Lead for Platform & DevOps

1 мес. назад

United StatesCanadaWorldwideLeadHybrid

python

Lead software engineer role focused on building core infrastructure and orchestration frameworks for AI hardware platforms.

О компании

The Core Infrastructure team builds the software systems that power engineering workflows across .
Our infrastructure coordinates complex work across machines, clusters, development environments, and hardware systems. We build orchestration frameworks, execution engines, scheduling systems, test infrastructure, developer tools, and reusable software platforms that allow engineers to build, test, qualify, and deliver software reliably at scale.
These systems are primarily built in Python, but the work goes far beyond scripting or automation. Our frameworks act as the control plane for distributed workflows, managing resources, execution state, concurrency, failures, retries, dependencies, and observability across large and complex environments.

Обязанности

We are hiring a Software Engineer to build and operate the platform layer behind engineering infrastructure.
You will work on CI/CD systems, Kubernetes, deployment automation, cloud and on-premises infrastructure, developer environments, artifact management, and observability. You will help make the systems engineers depend on reliable, scalable, and easy to operate.
This is an engineering-focused infrastructure role rather than a primarily ticket-driven operations position. You will automate repeated work, debug failures across system boundaries, and turn operational problems into durable software and platform improvements.
We value strong systems fundamentals, independent problem solving, and sound engineering judgment more than familiarity with any particular infrastructure product.
Design, build, and maintain CI/CD systems supporting build, test, integration, qualification, and release workflows.
Build and operate Kubernetes-based platforms and services used by engineering teams across .
Develop deployment systems, internal tools, and self-service workflows that make infrastructure changes repeatable, reviewable, and safe.
Improve infrastructure reliability, capacity, performance, cost efficiency, monitoring, and operational readiness.
Debug issues spanning CI pipelines, Kubernetes workloads, networking, storage, authentication, operating systems, and distributed applications.
Perform root-cause analysis and implement lasting fixes rather than relying on repeated manual intervention.
Partner with software, IT, security, networking, release, and developer-productivity teams to deliver scalable infrastructure solutions.

Требования

3+ years of professional experience in platform engineering, DevOps, infrastructure engineering, site reliability engineering, or software engineering.
Hands-on experience building or maintaining CI/CD pipelines and automated software-delivery workflows.
Experience deploying and operating services using Kubernetes and containerized environments.
Experience with a major cloud platform, preferably AWS, and programmatic infrastructure provisioning.
Strong understanding of Linux or Unix operating-system fundamentals.
Understanding of networking concepts such as DNS, routing, load balancing, proxies, ports, TLS, and service connectivity.
Proficiency in Python, Shell, or another language used to build infrastructure automation and operational tooling.
Experience with monitoring, logging, alerting, dashboards, and incident investigation.
Strong debugging and problem-solving skills, including the ability to investigate issues spanning applications, infrastructure, networking, and operating systems.

Будет плюсом

Experience with infrastructure-as-code tools, specifically Terraform.
Experience with Kubernetes controllers, operators, custom resources, Helm, Argo CD, or similar platform technologies.
Experience managing artifact repositories, package registries, build caches, or software-distribution infrastructure.
Familiarity with build systems, dependency management, and reproducible-build practices.
Experience supporting hybrid environments spanning cloud infrastructure, on-premises systems, and specialized hardware.
Experience with identity and access management, secrets, certificates, TLS, or mTLS.
Experience building internal developer platforms or self-service infrastructure products.
BS/MS in Computer Science or a related field, or equivalent practical experience.

Условия

People who are serious about software make their own hardware. At , we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined :
Build a breakthrough AI platform beyond the constraints of the GPU.
Publish and open source their cutting-edge AI research.
Work on one of the fastest AI supercomputers in the world.
Enjoy job stability with startup vitality.
Our simple, non-corporate work culture that respects individual beliefs.
Find out more about what it's like to work at here !
Apply today and become part of the forefront of groundbreaking advancements in AI!
Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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Другое

Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.