Immerse yourself in Ford's center of innovation and growth, working alongside top engineering, design, and software talent to create groundbreaking EVs and contribute to key technologies like batteries, charging, and recycling.

The UEV IVI Infra team works on Board bring up, Firmware, Bootloader, Hypervisor, Yocto, AAOS, Audio, Display, Touch, WiFi and Bluetooth connectivity, OTA Orchestrator, Diagnostics, Safety Island, Vehicle Access.

As a member of this team, you will learn the development workflow and help teams with development, debug, and testing of features.

To succeed in this role, you will need experience developing software and the ability to quickly learn new technology/domains.

C or C++ or Rust programming experience, experience developing software using Linux-based development environments, and experience working with embedded domains with Raspberry Pi/Arduino-like boards are highly desirable.

This position is a salary grade 4. For more information on salary and benefits, please visit [https://fordcareers.co/supplemental](https://fordcareers.co/supplemental).

Visa sponsorship is not available for this position.

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Your expertise in low-level system programming, kernel optimization, and virtualization technologies will be crucial in ensuring Anthropic can scale our compute infrastructure efficiently and reliably for training and serving frontier AI models.

Responsibilities:

Optimize our virtualization stack, improving performance, reliability, and efficiency of our VM environments

Design and implement kernel modules, drivers, and system-level components to enhance our compute infrastructure

Investigate and resolve performance bottlenecks in virtualized environments

Collaborate with cloud engineering teams to optimize interactions between our workloads and underlying hardware

Develop tooling for monitoring and improving virtualization performance

Work with our ML engineers to understand their computational needs and optimize our systems accordingly

Contribute to the design and implementation of our next-generation compute infrastructure

Share knowledge with team members on low-level systems programming and Linux kernel internals

Partner with cloud providers to influence hardware and platform features for AI workloads

You may be a good fit if you:

Have experience with Linux kernel development, system programming, or related low-level software engineering

Understand virtualization technologies (KVM, Xen, QEMU, etc.) and their performance characteristics

Have experience optimizing system performance for compute-intensive workloads

Are familiar with modern CPU architectures and memory systems

Have strong C/C++ programming skills and ideally experience with systems languages like Rust

Understand Linux resource management, scheduling, and memory management

Have experience profiling and debugging system-level performance issues

Are comfortable diving into unfamiliar codebases and technical domains

Are results-oriented, with a bias towards practical solutions and measurable impact

Care about the societal impacts of AI and are passionate about building safe, reliable systems

Strong candidates may also have experience with:

GPU virtualization and acceleration technologies

Cloud infrastructure at scale (AWS, GCP)

Container technologies and their underlying implementation (Docker, containerd, runc, OCI)

eBPF programming and kernel tracing tools

OS-level security hardening and isolation techniques

Developing custom scheduling algorithms for specialized workloads

Performance optimization for ML/AI specific workloads

Network stack optimization and high-performance networking

Experience with TPUs, custom ASICs, or other ML accelerators

Representative projects:

Optimizing kernel parameters and VM configurations to reduce inference latency for large language models

Implementing custom memory management schemes for large-scale distributed training

Developing specialized I/O schedulers to prioritize ML workloads

Creating lightweight virtualization solutions tailored for AI inference

Building monitoring and instrumentation tools to identify system-level bottlenecks

Enhancing communication between VMs for distributed training workloads

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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.

Responsibilities:

We are seeking a Linux OS and System Programming Subject Matter Expert to join our Infrastructure team. In this role, you'll work on accelerating and optimising our virtualisation and VM workloads that power our AI infrastructure. Your expertise in low-level system programming, kernel optimisation, and virtualisation technologies will be crucial in ensuring Anthropic can scale our compute infrastructure efficiently and reliably for training and serving frontier AI models.

• Optimise our virtualisation stack, improving performance, reliability, and efficiency of our VM environments
• Design and implement kernel modules, drivers, and system-level components to enhance our compute infrastructure
• Investigate and resolve performance bottlenecks in virtualised environments
• Collaborate with cloud engineering teams to optimise interactions between our workloads and underlying hardware
• Develop tooling for monitoring and improving virtualisation performance
• Work with our ML engineers to understand their computational needs and optimise our systems accordingly
• Contribute to the design and implementation of our next-generation compute infrastructure
• Share knowledge with team members on low-level systems programming and Linux kernel internals
• Partner with cloud providers to influence hardware and platform features for AI workloads

You may be a good fit if you:

• Have experience with Linux kernel development, system programming, or related low-level software engineering
• Understand virtualisation technologies (KVM, Xen, QEMU, etc.) and their performance characteristics
• Have experience optimising system performance for compute-intensive workloads
• Are familiar with modern CPU architectures and memory systems
• Have strong C/C++ programming skills and ideally experience with systems languages like Rust
• Understand Linux resource management, scheduling, and memory management
• Have experience profiling and debugging system-level performance issues
• Are comfortable diving into unfamiliar codebases and technical domains
• Are results-oriented, with a bias towards practical solutions and measurable impact
• Care about the societal impacts of AI and are passionate about building safe, reliable systems

Strong candidates may also have experience with:

• GPU virtualisation and acceleration technologies
• Cloud infrastructure at scale (AWS, GCP)
• Container technologies and their underlying implementation (Docker, containerd, runc, OCI)
• eBPF programming and kernel tracing tools
• OS-level security hardening and isolation techniques
• Developing custom scheduling algorithms for specialised workloads
• Performance optimisation for ML/AI specific workloads
• Network stack optimisation and high-performance networking
• Experience with TPUs, custom ASICs, or other ML accelerators

Representative projects:

• Optimising kernel parameters and VM configurations to reduce inference latency for large language models
• Implementing custom memory management schemes for large-scale distributed training
• Developing specialised I/O schedulers to prioritise ML workloads
• Creating lightweight virtualisation solutions tailored for AI inference
• Building monitoring and instrumentation tools to identify system-level bottlenecks
• Enhancing communication between VMs for distributed training workloads

Deadline to apply:

None. Applications will be reviewed on a rolling basis.

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.

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.

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 the authenticity of an email or a request, please reach out to us directly.

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What you'll do

Your main responsibilities will be to architect, build, and optimise ultra-low-latency search infrastructure using Rust. You will also profile and instrument services, continuously driving down response times at scale, and develop and maintain distributed backend components powering real-time search and retrieval.

• Architect, build, and optimise ultra-low-latency search infrastructure using Rust
• Profile and instrument services, continuously driving down response times at scale
• Develop and maintain distributed backend components powering real-time search and retrieval

What you need

To be successful in this role, you will need to have deep expertise in Rust programming, especially for backend/search systems. You will also need experience profiling and tuning high-load, low-latency distributed services, and a strong understanding of systems design, Linux internals, and performance debugging.

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