About the Role 

We are building a high-performance SRE function to support one of the world’s fastest-growing AI inference services, powered by the Wafer-Scale Engine (WSE). This team will help deliver world-class, ultra-reliable inference infrastructure for leading model builders such as OpenAI and other frontier labs. 

As a Staff SRE, you will lead the engineering effort to eliminate toil at scale by driving implementation of self-service delivery pipelines, shared observability common tooling. This role starts with ~1 month of hands-on operational immersion to gain deep familiarity with our current stack, production pain points, and high-stakes workflows.  

From there, your primary focus shifts to architecting and delivering the "tomorrow" layer: declarative GitOps-driven CD for model releases, capacity provisioning and cluster upgrades. Success over the first year in this role will be defined by enabling core teams, product managers, external customers, and cluster stakeholders to operate in a fully self-service model with strong reliability guarantees. 

You will partner with our early-career SRE sub-team, who own day-to-day operations. This will allow you to deeply understand their pain points, automate their toil, and mentor them as platform engineers.  

You will collaborate with the tech leads and the leadership team across core, cluster, cloud, and product stakeholders. This work will shift reliability from an ops-only burden to a shared engineering discipline that underpins frontier AI inference at scale. 

If you are a proven Staff+ engineer who enjoys turning complexity into elegant reliability at scale, this is your chance to lead this transformation from the front. 

This role does not require 24/7 on-call rotations. 

Key Responsibilities 

• Define and implement a robust strategy for delivering and running software reliably and at scale across multiple datacenters and cloud-based solutions. 

• Architect self-service platforms and internal tooling that let product teams, external customers, and cluster operators safely trigger and observe critical workflows with minimal handoffs.  

• Define and evolve reliability practices for inference workloads, including SLOs and SLIs for latency, throughput, and accuracy stability; error budgets; blameless postmortems; chaos testing; and capacity forecasting across multi-datacenter and on-prem environments.  

• Mentor mid-level SREs, support critical incident escalations, and use production pain points to prioritize the highest-leverage automation work.  

• Measure and drive impact through clear metrics, including toil reduction, deployment velocity, SLO compliance, MTTR, and adoption of self-service workflows. 

Required Experience & Skills 

• 8+ years in SRE, infrastructure engineering, or platform engineering, with a strong record of improving automation and reliability at large scale in FAANG, hyperscaler, or similarly demanding environments.  

• Deep expertise operating large scale heterogenous clusters with a proprietary cloud control plane 

• Proven track record designing and delivering CI/CD or GitOps systems using Argo CD or similar tools, with strong safety and observability built in.  

• Hands-on experience with observability systems such as Loki, Tempo, Mimir, and Prometheus  

• Ability to lead complex projects end to end, influence cross-functional stakeholders, and communicate technical direction clearly. 

Nice-to-Haves 

• Experience with Bazel or other large-scale build systems in production.  

• Background in AI/ML inference systems, including model serving runtimes, GPU or wafer-scale orchestration, latency and accuracy SLOs, or drift monitoring.  

• Prior work on predictive autoscaling, chaos engineering, or cost-aware capacity planning for compute-intensive workloads.  

Location   

• SF Bay Area 

• Toronto 

In late 2024, we launched Cerebras Inference, the fastest Generative AI inference service in the world, over 10 times faster than GPU-based hyperscale cloud inference. Since launch, we’ve scaled to meet the surging demand from AI labs, enterprises, and a thriving developer community. 

In October 2025, we announced our series G funding, raising $1.1 billion USD to accelerate the expansion of our products and services to meet global AI demand. 

About the team 

The Cerebras Inference team’s mission is to deliver the world’s most performant, secure, and reliable enterprise-grade AI service. We build and operate large-scale distributed systems that power AI inference at unprecedented speed and efficiency. Join us to help scale inference and accelerate AI. 

About the role 

We’re looking for a hands-on Reliability Tech Lead (IC) to own the mission of making Cerebras Inference the most reliable AI service in the world. You will drive reliability strategy and execution across our inference stack, from client SDKs and public-cloud multi-region deployments to wafer-scale systems in specialized data centers. 

In this role, you will define SLOs and incident-response frameworks, design and implement reliability mechanisms at scale, and partner across hundreds of engineers to ensure our service meets world-class reliability standards. 

If you are passionate about building and operating massive-scale, low-latency, high-reliability distributed systems, we want to hear from you. 

Responsibilities: 

• Define and drive reliability strategy: establish SLOs and ensure alignment across engineering. 

• Design and implement reliability mechanisms: build and evolve systems for fault detection, graceful degradation, failover, throttling, and recovery across multiple regions and data centers. 

• Lead large-scale incident management: own postmortems, root-cause analysis, and prevention loops for reliability-related incidents. 

• Architect for reliability and observability: influence system design for redundancy, durability, and debuggability. 

• Develop reliability tooling: create internal tools and frameworks for chaos testing, load simulation, and distributed fault injection. 

• Collaborate broadly: work across software, infrastructure, and hardware teams to ensure reliability is embedded into every layer of our inference service. 

• Monitor and communicate reliability metrics: build dashboards and alerts that measure service health and provide actionable insights. 

• Mentor and influence: guide engineers and set best practices for designing, testing, and operating reliable large-scale systems. 

Skills & Qualifications: 

• Bachelor's or master's degree in computer science or related field. 

• 7+ years of experience in backend, infrastructure, or reliability engineering for large-scale distributed systems. 

• Strong programming skills in at least one popular backend programming language such as Python, C++, Go, or Rust. 

• Deep and hard-earned experience of reliability principles: SLO/SLI/SLA design, incident response, and postmortem culture. 

• Excellent communication and cross-functional leadership skills. 

• Bonus: prior experience building large-scale AI infrastructure systems. 

About The Role

The Inference ML Engineering team at Cerebras Systems is dedicated to enabling our fast generative inference solution through simple APIs powered by a distributed runtime that runs on large clusters of our own hardware. Our mission is to empower enterprises, developers, and researchers to unlock the full potential of our platform, leveraging its performance, scalability, and flexibility. The team works closely with cross-functional groups, including compiler developers, cluster orchestrators, ML scientists, cloud architects, and product teams, to deliver high-impact solutions that redefine the boundaries of ML performance and usability.

As a Senior Software Engineer on the Inference ML Engineering team, you will play a key role in designing and implementing APIs, ML features, and tools that enable running state-of-the-art generative AI models on our custom hardware. You will architect solutions that enable seamless model translation and execution, ensuring high throughput and low latency, while maintaining ease of use. Your responsibilities will include leading technical initiatives, collaborating with other engineering teams to enhance the developer experience, enabling key ML features at scale, maintaining our speed advantage, achieving high throughput, and supporting a wide range of ML workloads. This role offers an opportunity to shape the evolution of our ML ecosystem while tackling complex technical challenges at the intersection of machine learning, software, and hardware.

Responsibilities

• Drive and provide technical guidance to a team of software engineers working on complex machine learning integration projects.
• Design and implement ML features (e.g., structured outputs, biased sampling, predicted outputs) that improve performance of generative AI models at inference time.
• Design and implement high-throughput, low-latency multimodal inference models that support delivery of image, audio, and video inputs and outputs.
• Maintain our scalable serving backend for handling many concurrent requests per minute.
• Scale our inference service by implementing detailed observability throughout the entire stack.
• Analyze and improve latency, throughput, memory usage, and compute efficiency on the service and the implementation of various features.
• Optimize software to accelerate generative LLM inference by achieving high throughput and low latency.
• Stay up-to-date with advancements in machine learning and deep learning, and apply state-of-the-art techniques to enhance our solutions.
• Evaluate trade-offs between different approaches, clearly articulate design choices, and develop detailed proposals for implementing new features.
• Uncover, scope, and prioritize significant areas of technical debt across the software stack to ensure continued high quality of the inference service.
• Build and maintain robust automated test suites to ensure software quality, performance, and reliability.
• Contribute to an agile team environment by delivering high-quality software and adhering to agile development practices.
• Lead cross-functional initiative across the company to deliver high-quality inference solutions.

Skills and Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Mathematics, or a related field.
• 8+ years of experience in large-scale software engineering, with a focus on deep learning or related domains.
• Proficiency in Python for building and maintaining scalable systems.
• Advanced proficiency in C++, with an emphasis on multi-threaded programming, performance optimization, and system-level development.
• Demonstrated experience driving cross-functional projects.
• Experience building and scaling large-scale inference systems for LLMs or multimodal models.
• Familiarity with LLM serving frameworks, such as vLLM, SGLang, and TensorRT-LLM.
• Solid understanding of software architectural patterns for large-scale, high-performance applications.
• Hands-on experience with ML frameworks, such as PyTorch, and a strong understanding of their underlying architectures.
• Strong problem-solving skills, with the ability to balance technical depth with practical implementation constraints.
• Exceptional communication and presentation skills, with the ability to work both independently and collaboratively across multidisciplinary teams.

About the Role 

We are building a high-performance SRE function to support one of the world’s fastest-growing AI inference services, powered by the Wafer-Scale Engine (WSE), helping deliver infrastructure for frontier-class models from leading model builders such as OpenAI. 

This role offers immediate ownership of real production systems at a growing scale, direct mentorship from seasoned engineers, and close collaboration with incoming Staff SREs who will focus on long-term automation. After ~1 month of shared hands-on operations with the Staff engineers, you’ll primarily operate the current setup, bring up new capacity in high-stakes environments and help bring new continuous delivery pipelines into production use. 

If you thrive in high-ownership SRE roles at scale and want to help shape a team from the ground up in cutting-edge AI Inference infrastructure, this is your chance. 

This role does not require 24/7 on-call rotations. 

Key Responsibilities 

• Remain hands-on with operational execution (releases, capacity changes, cluster upgrades) over the next year as we build robust continuous delivery pipelines and self-service capabilities 

• Contribute to the development of self-service CD pipelines for key workflows using our stack: Kubernetes, Bazel, Prometheus/Grafana/InfluxDB, Python, and Go. 

• Build reusable automation and internal developer tools that minimize operational toil and cross-team friction 

• Develop and extend telemetry, observability and alerting solutions to ensure operational reliability at scale 

• Collaborate with Cluster Ops and development teams to identify high-impact automation opportunities and iterate quickly 

• Contribute to reliability practices (SLOs, post-mortems, capacity planning) 

Required Experience & Skills 

• 2-4+ years in SRE with a strong operations or automation focus 

• Production Kubernetes experience 

• Solid Python or Go for building tools and automation 

• Proficiency with Prometheus, Grafana, and observability-driven workflows 

• Ability to measure and communicate impact – reliability metrics, operational toil, velocity gains 

Nice-to-Have 

• Hands-on GitOps expertise, Argo CD / Flux or equivalent, is a plus 

• Experience with building continuous delivery pipelines is a strong plus 

• Experience with Bazel or similar build systems is a strong plus. 

• Familiarity with capacity planning, on-prem or multi-datacenter environments 

Location   

• SF Bay Area 

• Toronto 

Location: Sunnyvale 

We're hiring a Staff Engineer to own major areas of the architecture of our Inference Cloud Platform. This team owns the cloud layer behind our Inference Service, with responsibility for availability, latency, reliability, and global scale. 

This is a hands on IC role for an engineer who wants to work on the hardest distributed systems problems in the stack: multi-region traffic architecture, graceful degradation under bursty AI workloads, performance at high QPS, and the operating model for a platform that has to stay fast and available under load. You'll write code, lead key architectural decisions in your domain, debug production issues, and help shape technical direction across adjacent teams. 

If you're interested in building the next-generation architecture of a globally distributed inference platform, we'd like to talk. 

Responsibilities 

• Platform Direction. Help shape the technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas. 

• Core Cloud Systems. Design and build critical platform components such as service discovery, request routing, load balancing, caching, batching, and traffic management for AI inference workloads. 

• Reliability & Performance. Architect active-active systems with rapid failover, graceful degradation, and clear SLOs. Drive system-level improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand. 

• Traffic Control & Service Tiers. Define platform mechanisms for admission control, quota management, rate limiting, and differentiated quality of service across workload types and customer tiers. 

• Execution on Critical Paths. Write and review production code in the most important parts of the platform. Make high-consequence architectural decisions within your area and set the technical bar through design reviews, code reviews, and sound engineering judgment. 

• Production Leadership. Lead on the hardest production issues and cross-system bottlenecks. Drive observability, incident response, capacity planning, and post-incident improvement with a high standard for operational rigor.  

• Technical Influence. Partner with ML, Product, Infrastructure, and Platform teams to translate product and business requirements into scalable system designs, and drive alignment on shared technical decisions within your domain and adjacent platform surfaces. 

• Mentorship. Raise the effectiveness of senior engineers through design feedback, pairing, and clear technical standards. 

Skills & Qualifications 

• 8+ years of experience in software engineering, with substantial individual contributor experience building and operating large-scale distributed systems or cloud infrastructure. 

• Deep expertise in distributed systems architecture in cloud environments, including networking, compute orchestration, container platforms, and multi-region production services. 

• Strong track record of making sound architectural decisions for highly available, latency-sensitive systems at scale. 

• Experience optimizing latency, throughput, and efficiency in high-QPS systems. Experience with TTFT and tail-latency reduction is a strong plus. 

• Strong proficiency in backend or systems languages such as Go, C++, or Python, with the expectation that you can contribute production code directly. 

• Experience designing observability and reliability practices, including metrics, logging, tracing, alerting, incident response, and SLO-driven operations. 

• Ability to influence senior engineers and cross-functional partners through technical credibility, communication, and judgment, especially within your domain and adjacent systems. 

• Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads is a plus. 

Location: Sunnyvale 

We're hiring a Principal Engineer for our Inference Cloud Platform. This team owns the cloud layer behind our Inference Service, including availability, latency, reliability, and multi-region scale. 

This is one of the most senior IC roles on the team, for someone who can identify the highest-leverage platform problems, set direction across multiple teams, define long-term architecture, and write production code on critical paths. 

Many of the key decisions are ambiguous at the outset; you’ll need to frame the problem, make tradeoffs, and drive execution without a clear spec. 

The scope includes multi-region traffic architecture, graceful degradation under bursty AI workloads, high-QPS performance, and the operating model for a platform that needs to remain fast and available under changing demand. You'll partner closely with ML, Product and Infrastructure teams. 

Responsibilities 

• Problem Definition & Prioritization. Identify the most important technical problems for the platform, often before there's a clear ask. Make explicit tradeoff decisions about what the platform will and won't support, with reasoning that holds up under scrutiny from senior engineering leadership. 

• Platform Direction. Set the long-term technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time. 

• Reliability & Performance. Architect active-active systems with rapid failover and graceful degradation (circuit breaking, backpressure, load shedding) with clear SLOs. Drive improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand. 

• Code & Design Reviews. Contribute production code in critical paths, review designs and implementations, and make architectural decisions including build-vs-buy tradeoffs with long-term operational consequences. 

• Production Leadership. Lead on the hardest production issues and cross-system bottlenecks. Drive observability, incident response, capacity planning, and post-incident improvement with a high standard for operational rigor. 

• Technical Strategy Beyond Your Team. Drive platform-wide decisions across adjacent teams on reliability, API design, capacity planning, and deployment strategy through strong technical judgment. Translate product and business requirements into scalable system designs and drive alignment on shared infrastructure decisions. 

• Mentorship. Raise the quality of technical decision-making across teams through design feedback, pairing, and clear engineering standards. 

Skills & Qualifications 

• 10+ years of experience in software engineering, with substantial individual contributor experience building and operating large-scale distributed systems or cloud infrastructure.  

• Deep expertise in distributed systems architecture in cloud environments, including networking, compute orchestration, container platforms, and multi-region production services. 

• Strong track record of making sound architectural decisions for highly available, latency-sensitive systems at scale, demonstrated through systems you built directly. 

• Experience optimizing latency, throughput, and efficiency in high-QPS systems. Experience with TTFT and tail-latency reduction is a strong plus. 

• Strong proficiency in backend or systems languages such as Go, C++, or Python, with the expectation that you can contribute production code directly. 

• Experience designing observability and reliability practices, including metrics, logging, tracing, alerting, incident response, and SLI/SLO/SLA-driven operations. 

• Ability to influence senior engineers, technical leads, and cross-functional partners through technical credibility, communication, and judgment. 

• Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads is a plus.