Sr. Staff Software Engineer, Inference Platform
Staff-level engineer on Cerebras's Inference Platform team, designing and writing production code for the orchestration layer that runs AI inference across datacenter clusters. Core work spans Kubernetes operators, secure service policies, CI/CD, and reliability/latency optimization of large-scale distributed systems, primarily in Go or C++.
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Onsite
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 8+ years of software engineering experience, with substantial IC experience building and operating large-scale distributed systems or cloud infrastructure
- Deep expertise in distributed systems architecture, ideally with Kubernetes
- Track record of sound architectural decisions for highly available, latency-sensitive systems at scale
- Experience with security (certificates, TLS, mTLS)
- Experience optimizing latency, throughput, and efficiency in high-QPS systems
- Strong proficiency in backend or systems languages such as Go or C++, able to contribute production code directly
- Experience designing observability and reliability practices: metrics, logging, tracing, alerting, incident response, SLO-driven operations
- Ability to influence senior engineers and cross-functional partners through technical credibility, communication, and judgment
Nice to have
- Experience with TTFT and tail-latency reduction
- Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads
Full description
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras 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.
Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
About the Role
We're hiring a Staff Engineer to help lead, drive, and contribute to projects on our Inference Platform team. Our team primarily owns the orchestration layer that runs inference on our datacenter clusters, connecting cloud components with machine learning services. We are often the first team to face problems that haven't been solved yet, leading solutions across Kubernetes operators, service security policies, and CI/CD.
If you're interested in building the next-generation architecture of a globally distributed inference platform, we'd like to talk.
Responsibilities
- Design, develop, test, and maintain production software, with responsibilities spanning testing, continuous development, observability, security, networking, debugging, and productionization.
- Raise the effectiveness of senior engineers through design feedback, pairing, and clear technical standards.
- Platform Direction. Help shape the technical direction for the Inference Platform, Kubernetes custom resource definitions, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas.
- 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.
- 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 Cloud 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.