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.

About the role

Anthropic runs some of the largest Kubernetes clusters in the industry. We have fleets of hundreds of thousands of nodes across multiple cloud providers and datacenters to train, research, and serve frontier AI models. The Kubernetes Platform team owns the Kubernetes control plane that makes those clusters work.

We are operating at a scale where the defaults stop working. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. We scale the control plane itself , apiserver, etcd, controllers , so it stays responsive as object counts and node counts grow by orders of magnitude. And we build the core cluster services every workload depends on, like service discovery, so they hold up under the same pressure.

We make sure the control plane is fast, correct, and always available. Your work will directly determine whether Anthropic can keep reliably and safely training frontier models as our compute footprint continues to grow.

Key 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

• Scale the Kubernetes control plane (apiserver, etcd, controller-manager) to support clusters far beyond typical limits, and find the next bottleneck before it finds us

• Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on

• Build and maintain custom controllers, operators, and CRDs

• Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities

• Collaborate with cloud providers on required features and escalations

• Participate in on-call, lead incident response, and design processes (postmortems, runbooks, SLOs) that help the team avoid repeating failures

Minimum 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++)

• Deep, hands-on Kubernetes experience (well beyond "user of") into scheduler, controllers, apiserver, or operating large multi-tenant clusters

• Demonstrated ability to debug complex issues across the stack, from API behavior down to node and network-level root causes

• A track record of designing for reliability, correctness, and clear failure semantics in systems other engineers depend on

• Strong written and verbal communication; comfort building consensus with internal stakeholders

Preferred qualifications

• Experience with Kubernetes internals or contributions: kube-scheduler / scheduling framework, apiserver, etcd, client-go, controller-runtime, or similar

• Experience building or operating cluster schedulers or batch systems (e.g., Kueue, Volcano, Slurm, or in-house equivalents)

• Background scaling control planes or coordination systems (etcd, ZooKeeper, Consul, or large DNS/service-mesh deployments)

• Familiarity with ML infrastructure: GPUs, TPUs, or Trainium; gang scheduling; topology-aware placement; 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

The annual compensation range for this role is listed below.

Annual Salary: £325,000-£485,000 GBP

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

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 encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you’re interested in this work. We think AI systems like the ones we’re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you’re ever unsure about a communication, don’t click any links,visit anthropic.com/careers directly for confirmed position openings.

How we’re different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We’re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. 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. Guidance on Candidates’ AI Usage: Learn about our policy for using AI in our application process

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Anthropic runs some of the largest Kubernetes clusters in the industry. We have fleets of hundreds of thousands of nodes across multiple cloud providers and datacenters to train, research, and serve frontier AI models. The Kubernetes Platform team owns the Kubernetes control plane that makes those clusters work.

We are operating at a scale where the defaults stop working. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. We scale the control plane itself , apiserver, etcd, controllers , so it stays responsive as object counts and node counts grow by orders of magnitude. And we build the core cluster services every workload depends on, like service discovery, so they hold up under the same pressure.

Key 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

• Scale the Kubernetes control plane (apiserver, etcd, controller-manager) to support clusters far beyond typical limits, and find the next bottleneck before it finds us

• Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on

• Build and maintain custom controllers, operators, and CRDs

• Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities

• Collaborate with cloud providers on required features and escalations

• Participate in on-call, lead incident response, and design processes (postmortems, runbooks, SLOs) that help the team avoid repeating failures

Minimum 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++)

• Deep, hands-on Kubernetes experience (well beyond "user of") into scheduler, controllers, apiserver, or operating large multi-tenant clusters

• Demonstrated ability to debug complex issues across the stack, from API behavior down to node and network-level root causes

• A track record of designing for reliability, correctness, and clear failure semantics in systems other engineers depend on

• Strong written and verbal communication; comfort building consensus with internal stakeholders

Preferred qualifications

• Experience with Kubernetes internals or contributions: kube-scheduler / scheduling framework, apiserver, etcd, client-go, controller-runtime, or similar

• Experience building or operating cluster schedulers or batch systems (e.g., Kueue, Volcano, Slurm, or in-house equivalents)

• Background scaling control planes or coordination systems (etcd, ZooKeeper, Consul, or large DNS/service-mesh deployments)

• Familiarity with ML infrastructure: GPUs, TPUs, or Trainium; gang scheduling; topology-aware placement; 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

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

• 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.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. 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.

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Our mission is to democratize finance for all.

An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history.

If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact.

Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers.

We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Data Platform organization builds and operates the systems that power how data is stored, moved, and consumed across Robinhood.

This organization spans three core pillars: Storage (Postgres, DynamoDB, and caching systems), Streaming (real-time event infrastructure), and Data Lake (ingestion and compute systems built on Delta Lake).

Together, these platforms support transactional workloads, real-time data processing, and large-scale analytics that are critical to Robinhood’s products and operations.

The team owns the full lifecycle of data,from low-latency order path systems to near real-time and batch analytics,serving millions of users and internal teams across the company.

As a Senior Staff Software Engineer, you will serve as the technical lead across the Data Platform organization, shaping architecture and guiding execution across multiple teams.

You’ll work on complex distributed systems challenges such as database sharding and proxy architectures, real-time streaming and CDC systems, and large-scale data ingestion and compute platforms.

You’ll define and drive key technical bets, partner with engineering leaders to align platform capabilities with business needs, and lead 0→1 initiatives that introduce new capabilities across storage, streaming, and data systems.

This is a rare opportunity to influence multiple critical systems at once while raising the technical bar across an entire organization!

What you’ll do

Lead architectural direction across storage, streaming, and data lake platforms, connecting systems that handle transactional, real-time, and analytical workloads.

Design and guide implementation of distributed systems, including database sharding, proxy-based query routing, and real-time event processing pipelines.

Improve data freshness and latency by evolving streaming and ingestion systems toward near real-time processing goals.

Partner with engineering leaders and teams across Robinhood to define platform strategy, align roadmaps, and ensure systems meet reliability, scalability, and performance requirements.

Drive 0→1 initiatives that introduce new platform capabilities, including next-generation streaming, CDC, and data processing systems.

What you bring

Extensive experience building and scaling distributed systems, with deep expertise in at least two of the following areas: storage systems, streaming platforms, or data lake / large-scale data processing.

Strong understanding of database systems such as PostgreSQL and/or DynamoDB, including replication, sharding, and performance optimization.

Experience with streaming and event-driven architectures using technologies such as Kafka, Flink, or similar systems.

Familiarity with modern data platforms and compute engines such as Spark, Delta Lake, or equivalent large-scale data processing systems.

Proven ability to lead complex technical initiatives, define long-term architecture, and collaborate across multiple teams.

What we offer

Challenging, high-impact work to grow your career.

Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching.

Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents.

Lifestyle wallet – a highly flexible benefits spending account for wellness, learning, and more.

Employer-paid life & disability insurance, fertility benefits, and mental health benefits.

Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!

Exceptional office experience with catered meals, events, and comfortable workspaces.

In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.

Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands.

The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones.

For other locations not listed, compensation can be discussed with your recruiter during the interview process.

Base Pay Range:

Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC)$264,000-$310,000 USD

Zone 2 (Denver, CO; Westlake, TX; Chicago, IL)$264,000-$310,000 USD

Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL)$264,000-$310,000 USD

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As a Staff Data Engineer, you'll balance hands-on engineering with technical leadership. You'll architect solutions for orders of magnitude growth, mentor engineers across the organization, and drive strategic decisions about our data stack. You'll work closely with analytics, product, and engineering leadership to enable data-driven decision making at scale while building systems that serve millions of users and inform critical business decisions.

Our team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most.

Responsibilities

• Own and evolve our end-to-end event pipeline architecture, from Kafka ingestion through Snowflake analytics, setting technical direction for data infrastructure
• Design and architect distributed data systems that scale to orders of magnitude more data volume while maintaining world-class query performance
• Lead initiatives to build and optimize CDC (change data capture) pipelines and streaming data transformations at massive scale
• Establish best practices for data quality, pipeline reliability, and system observability across the organization
• Drive strategic technical decisions about data modeling, infrastructure architecture, and technology choices
• Mentor engineers and elevate data engineering practices across analytics, product, and engineering teams

Requirements

• 10+ years as a data or software engineer with deep expertise in distributed systems, data infrastructure, and high-growth SaaS products at massive scale
• Expert-level knowledge of Apache Kafka (producers, consumers, Kafka Connect, stream processing) and event streaming platforms
• Extensive hands-on experience with Snowflake, including performance optimization, cost management, and data modeling; strong foundation in Postgres, CDC patterns, and replication strategies
• Proven track record architecting and leading major data infrastructure initiatives through orders-of-magnitude growth
• Experience establishing best practices and driving technical strategy across organizations
• Strong communication skills with a history of influencing technical direction across engineering, analytics, and leadership
• Proficiency with dbt, Terraform, and working knowledge of data governance, privacy compliance (GDPR, CCPA), and security best practices

Compensation Range

The base salary for this full-time position, which spans multiple internal levels depending on qualifications, ranges between $230K - $310K plus benefits & equity.

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