At a Glance
- Tasks: Own and enhance Kubernetes scheduler for AI workloads, ensuring reliable model training.
- Company: Join a pioneering AI company focused on safe and beneficial technology.
- Benefits: Competitive pay, flexible hours, generous leave, and equity donation matching.
- Other info: Collaborative environment with strong focus on ethical AI and career growth.
- Why this job: Make a real impact in AI while working with cutting-edge technology.
- Qualifications: 8+ years in software engineering with experience in distributed systems and cluster schedulers.
The predicted salary is between 120000 - 140000 £ per year.
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. We have fleets of hundreds of thousands of nodes across multiple cloud providers and datacenters to train, research, and serve frontier AI models. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. Your work will directly determine whether Anthropic can keep reliably and safely training frontier models as our compute footprint continues to grow.
Responsibilities:
- Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets, including custom scheduling plugins and policies for gang scheduling, topology awareness, and preemption.
- Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on.
- Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities.
- Participate in on-call, lead incident response, and design processes (postmortems, runbooks, SLOs) that help the team avoid repeating failures.
Qualifications:
- Significant software engineering experience building and operating production distributed systems.
- Proficiency in at least one systems-appropriate language (e.g., Go, Python, Rust, or C++).
- Demonstrated ability to debug complex issues across the stack, from API behaviour down to node and network-level root causes.
- Comfort building consensus with internal stakeholders.
- Experience building or operating cluster schedulers or batch systems (e.g., background scaling control planes or coordination systems such as etcd, ZooKeeper, Consul, or large DNS/service-mesh deployments).
- Familiarity with ML infrastructure: gang scheduling; collective networking such as NCCL.
- Experience with GCP and/or AWS, including GKE/EKS internals and Infrastructure as Code.
- Low-level systems experience such as Linux kernel tuning, cgroups, or eBPF.
- 8+ years of relevant industry experience, including time leading large, ambiguous infrastructure projects.
Education:
- Bachelor’s degree or an equivalent combination of education, training, and/or experience in a field relevant to the role as demonstrated through coursework, training, or professional experience.
Location-based hybrid policy: Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
🌳🌳🌳Staff Software Engineer🌳🌳🌳 in London employer: Anthropic
Anthropic is an exceptional employer for those passionate about advancing reinforcement learning in a collaborative and innovative environment. With competitive compensation, generous vacation and parental leave, and flexible working hours, employees enjoy a supportive work culture that prioritises both personal and professional growth. Located in a vibrant office space, team members have the unique opportunity to engage directly with cutting-edge research while making meaningful contributions to the responsible scaling of AI.