This means driving deployments from scoping through production, running the platform work that spans them, and working across API, Platform Foundations, Security, our cloud provider counterparts, and whoever else is on the critical path when dependencies and tradeoffs pile up.

Responsibilities:

• Own end-to-end program execution for Anthropic’s API across major cloud deployments, from scoping through production launch and steady-state operations

• Drive the platform programs that cut across individual deployments: the shared foundations that get built once and reused, not rebuilt per cloud

• Act as a primary coordination point with cloud provider counterparts, keeping engagement clean across multiple internal teams with touchpoints into the same partner

• Partner with engineering leadership to turn technical direction into executable plans with clear owners, dependencies, and risk tracking

• Build the program scaffolding (roadmaps, status reporting, decision logs, escalation paths) that lets a fast-moving org stay aligned without slowing down

• Drive the hard sequencing conversations when partner commitments, engineering bandwidth, and priorities are in tension, and surface them to leadership with a recommendation

• Identify where program coverage is thin relative to the load and help shape how we staff around it

You may be a good fit if you:

• Have 10+ years of technical program management experience, including ownership of large infrastructure or platform programs with many engineering teams and external partners in the mix

• Have deep technical fluency in cloud APIs, infrastructure, distributed systems, or platform engineering, enough to be a credible partner to senior engineers on architecture and sequencing, not just a tracker of their decisions

• Have run programs spanning organizational boundaries where you had no direct authority over most of the people whose work you depended on, and delivered anyway

• Have direct experience with multi-cloud or hybrid cloud environments, large-scale migrations, or building platform abstraction layers

• Have worked with major cloud providers (AWS, GCP, Azure) or similar large technology partners, and know how to keep those relationships productive when priorities diverge

• Are comfortable operating in ambiguity on the long arc while being ruthlessly concrete on what ships this quarter and who owns it

• Have a track record of making a program get cheaper to run the second and third time, not just landing the first instance

• Thrive in environments where the plan you wrote last month needs rewriting, without losing the thread on what matters

Strong candidates may also:

• Have experience with production serving infrastructure, inference systems, or ML platform work

• Have moved between senior IC and management roles, or have interest in doing so

• Have worked at a company rebuilding systems and org in flight during rapid scale-up

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The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform—from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources—compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need engineers who can navigate these platform differences, build robust abstractions that work across providers, and make smart infrastructure decisions that keep us cost-effective at massive scale.

Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.

What You'll Do

• Design and build infrastructure that serves Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models
• Collaborate with CSP partner engineering teams to resolve operational issues, influence provider roadmaps, and stand up end-to-end serving on new cloud platforms
• Design and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions
• Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity
• Contribute to capacity planning and autoscaling strategies that dynamically match supply with demand across CSP validation and production workloads
• Optimize inference cost and performance across providers—designing workload placement and routing systems that direct requests to the most cost-effective accelerator and region
• Contribute to inference features that must work consistently across all platforms
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code or container orchestration
• Have strong interest in inference
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and self-driven, taking ownership of problems end-to-end with a bias toward flexibility and high-impact work
• Pick up slack, even when it goes outside your job description

Strong Candidates May Also Have Experience With

• Direct experience working with CSP partner teams to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings
• A background in building platform-agnostic tooling or abstraction layers that work across cloud providers
• Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments
• Strong familiarity with LLM inference optimization, batching, caching, and serving strategies
• Experience with Machine learning infrastructure including GPUs, TPUs, Trainium, or other AI accelerators
• Background designing and building CI/CD systems that automate deployment and validation across cloud environments
• Solid understanding of multi-region deployments, geographic routing, and global traffic management
• Proficiency in Python or Rust

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. 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.

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Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

• $295K – $555K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

OpenAI’s Inference team powers the deployment of our most advanced models - including our GPT models, 4o Image Generation, and Whisper - across a variety of platforms. Our work ensures these models are available, performant, and scalable in production, and we partner closely with Research to bring the next generation of models into the world. We're a small, fast-moving team of engineers focused on delivering a world-class developer experience while pushing the boundaries of what AI can do.

We’re expanding into multimodal inference, building the infrastructure needed to serve models that handle image, audio, and other non-text modalities. These workloads are inherently more heterogeneous and experimental, involving diverse model sizes and interactions, more complex input/output formats, and tighter coordination with product and research.

About the Role

We’re looking for a software engineer to help us serve OpenAI’s multimodal models at scale. You’ll be part of a small team responsible for building reliable, high-performance infrastructure for serving real-time audio, image, and other MM workloads in production.

This work is inherently cross-functional: you’ll collaborate directly with researchers training these models and with product teams defining new modalities of interaction. You'll build and optimize the systems that let users generate speech, understand images, and interact with models in ways far beyond text.

In this role, you will:

• Design and implement inference infrastructure for large-scale multimodal models.

• Optimize systems for high-throughput, low-latency delivery of image and audio inputs and outputs.

• Enable experimental research workflows to transition into reliable production services.

• Collaborate closely with researchers, infra teams, and product engineers to deploy state-of-the-art capabilities.

• Contribute to system-level improvements including GPU utilization, tensor parallelism, and hardware abstraction layers.

You might thrive in this role if you:

• Have experience building and scaling inference systems for LLMs or multimodal models.

• Have worked with GPU-based ML workloads and understand the performance dynamics of large models, especially with complex data like images or audio.

• Enjoy experimental, fast-evolving work and collaborating closely with research.

• Are comfortable dealing with systems that span networking, distributed compute, and high-throughput data handling.

• Have familiarity with inference tooling like vLLM, TensorRT-LLM, or custom model parallel systems.

• Own problems end-to-end and are excited to operate in ambiguous, fast-moving spaces.

Nice to Have:

• Experience working with image generation or audio synthesis models in production.

• Exposure to distributed ML training or system-efficient model design.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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RTL & Codesign Engineer

Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

• $225K – $445K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI.

About the Role

We’re looking for a RTL Engineer to design and implement key compute, memory, and interconnect components for our custom AI accelerator. You’ll work closely with architecture, verification, physical design, and ML engineers to translate AI workloads into efficient hardware structures. This is a hands-on design role with significant ownership across definition, modeling, and implementation.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role you will:

• Produce clean, production-quality microarchitecture and RTL for major accelerator subsystems

• Contribute to architectural studies including performance modeling and feasibility analysis.

• Collaborate with software, simulator, and compiler teams to ensure hardware/software co-design and workload fit.

• Partner with DV and PD to ensure functional correctness, timing closure, area/power targets, and clean integration.

• Build and review performance and functional models to validate design intent.

• Participate in design reviews, documentation, and bring-up support across the full silicon lifecycle.

You Might Thrive In This Role If You Have:

• Graduate-level research or industry experience in computer architecture, AI/ML hardware–software co-design, including workload analysis, dataflow mapping, or accelerator algorithm optimization.

• Expertise writing production-quality RTL in Verilog/SystemVerilog, with a track record of delivering complex blocks to tape-out.

• Experience developing hardware design models or architectural simulators, ideally for AI/ML or high-performance compute systems.

• Familiarity with industry-standard design tools (lint, CDC/RDC, synthesis, STA) and methodologies.

• Ability to work cross-functionally with architecture, ML systems, compilers, and verification teams.

• Strong problem-solving skills and ability to think across abstraction layers, from algorithms to circuits.

• Passion for building industry-leading massive-scale hardware systems.

_To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements as provided in those laws and regulations._

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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