In this role, you will push the boundaries of container orchestration far beyond existing systems like Kubernetes, manage exascale compute resources, optimize for high-performance training runs and production serving, and collaborate closely with research and systems teams to deliver reliable, ultra-scalable infrastructure that enables xAI's next-generation models and applications.

Responsibilities include building and managing massive-scale clusters, designing, developing, and extending an in-house container orchestration platform, collaborating with research teams to architect and optimize compute clusters, profiling, debugging, and resolving complex system-level performance bottlenecks, and owning end-to-end infrastructure initiatives.

To succeed in this role, you will need deep expertise in virtualization technologies and advanced containerization/sandboxing, strong proficiency in systems programming languages such as C/C++ and Rust, and proven track record profiling, debugging, and optimizing complex system-level performance issues.

Preferred skills and experience include experience in Linux kernel development, hypervisor extensions, or low-level system programming for compute-intensive workloads, operating or designing large-scale AI training/inference clusters, and familiarity with performance tools, tracing, and debugging in production distributed environments.

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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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Responsibilities:

• Build and manage massive-scale clusters to host, persist, train, and serve AI workloads with extreme reliability and performance.
• Design, develop, and extend an in-house container orchestration platform that achieves superior scalability, isolation, resource efficiency, and fault-tolerance compared to off-the-shelf solutions.
• Collaborate with research teams to architect and optimize compute clusters specifically for large-scale training runs, inference services, and real-time applications.
• Profile, debug, and resolve complex system-level performance bottlenecks, resource contention, scheduling issues, and reliability problems across the full stack.
• Own end-to-end infrastructure initiatives with first-principles design, rigorous testing, automation, and continuous optimization to support frontier AI compute demands.

Basic Qualifications:

• Deep expertise in virtualization technologies (KVM, Xen, QEMU) and advanced containerization/sandboxing (Kata, Firecracker, gVisor, Sysbox, or equivalent).
• Strong proficiency in systems programming languages such as C/C++ and Rust.
• Proven track record profiling, debugging, and optimizing complex system-level performance issues, with deep knowledge of Linux kernel internals, resource management, scheduling, memory management, and low-level engineering.
• Hands-on experience building or significantly enhancing distributed compute platforms, orchestration systems, or high-performance infrastructure at scale.

Preferred Skills and Experience:

• Experience in Linux kernel development, hypervisor extensions, or low-level system programming for compute-intensive workloads.
• Proven track record operating or designing large-scale AI training/inference clusters (GPU/TPU scale).
• Experience with custom runtimes, isolation techniques, or bespoke platforms for specialized AI compute.
• Familiarity with performance tools, tracing, and debugging in production distributed environments.

Compensation and Benefits:

$180,000 - $440,000 USD

Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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We are seeking a hands-on Senior System Software Engineer to join our Autonomous Vehicles Platform team. As part of our team, you will work on our Autonomous Driving Platform software, implementing performant and scalable solutions for data collection and autonomous vehicle fleets.

Our team is responsible for developing platform and middleware features for self-driving cars. This includes software that interacts with various sensors including Cameras, LIDAR, RADAR, GPS, IMU, Vehicle CAN etc. We are seeking candidates with interests in inventing, developing, and maintaining application framework and tools on multi-computer and heterogeneous architectures.

The role encompasses working with various teams across the stack, from platform and embedded software to cloud infrastructure, underpinned by safety and performance. It extends an opportunity to contribute to the technology that will drive the cars of the future!

What you'll be doing:

• Integrating sensors into our AV driving software stack.
• Develop and improve startup user experience of our AV stack
• Working on areas such as sensor abstraction layers, data processing and performance optimization, data serialization, and service frameworks.
• Collaborate with hardware, platform software, product, safety, performance, algorithms and cloud teams.

What we need to see:

• BS or MS degree in Computer Science, Electrical Engineering, or related field (or equivalent experience).
• 6+ year of professional experience working on system software.
• Excellent programming skills in C++, C and Python.
• Familiarity with source control tools.
• Solid understanding of Linux, QNX, ROS, and/or real-time operating systems.
• Experience in developing user-space system software, with a willingness to delve into kernel-space and/or low-level hardware when needed.
• Knowledge about embedded system programming, threading, mutex, synchronization, communication, and parallel computing to build highly-scalable and efficient applications.
• Familiarity with hardware architecture for CPU/GPU and memory alongside performance.
• Prior experience working in the following areas: Autonomous Vehicles, Robotics, Self-Driving-Cars, GPU technology, Embedded Systems, Computer Vision.
• Outstanding communication skills and teamwork.

Ways to stand out from the crowd:

• Deep understanding of system architecture, CPU/GPU/Memory/Storage, everything related to performance optimization.
• Experience in Autonomous Vehicle or Robotic System Building.
• Hand-on experience in embedded development, operating systems and real-time software.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 13, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

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Senior Electronics Design Engineer

610 - Electronics - Banbury, Oxfordshire

TGR Haas F1 Team has been a stalwart of the FIA Formula 1 World Championship over the past decade. With more than 200 grand prix starts to our name, we pride ourselves on being an ambitious challenger within Formula 1 - and we want you to be part of that journey.

The first American Formula 1 team to compete in the sport since 1986, TGR Haas F1 Team made an immediate impression with a memorable points-scoring debut at the 2016 Australian Grand Prix. Ten years later, the team is still building momentum, guided by clear objectives and technical partnerships, and fresh from securing its second biggest points haul in a Formula 1 season.

Yes, you’ll learn from us, but we expect to learn from you too!

General Summary:

This position reports to the Head of Electronics or their designee; located in Banbury, UK. Responsible for leading the team  on specification, design, development and project delivery of all electronics and electrical components both on and off car in accordance with the F1 technical regulations.

Key Responsibilities:

• Promote teamwork and effective communications to develop working relationships between all personnel and departments.
• Taking the lead and driving the design of on and off-car harnesses, units, sensors, plus other assemblies ensuring they meet performance and reliability requirements.
• Leading with the design of FIA, FOM and supplier component installations in accordance with their requirements, working closely with and advising the mechanical design office.
• Drive the specification and design of electronics systems to support on and off-car instrumentation and measurements.
• Generate and support the compilation of installation manuals and associated documentation to aid trackside installation of harnesses, units, sensors and associated components.
• Take a lead role to proactively investigate faults and generate fault reports working closely with the Reliability Engineer and the electronics trackside team.
• Generate mitigation solutions for fault resolutions of electronics, sensors and electrical components both on and off car.
• Continuously monitor and lead improvement of on and off car installations, design standards and operational processes and procedures.
• Support in a leading role, pre-season car build, test and rig events as required to oversee the installation of the electronics system in the chassis as well as garage environment.
• Actively engage in vehicle performance, development and reliability projects giving direction to the Mechanical Design Office for electrical and electronics solutions.
• Follow all safety regulations in all venues.
• Other duties as assigned by the Head of Electronics or their designee.

Education and Work Experience:

• University Degree or equivalent is essential, preferably in Electronics or Electrical Engineering.
• Multi-year experience of working in state-of-the art electronics engineering in a high-level technology development environment is required.
• Motorsport experience would be advantageous.

Specialized Knowledge and Skills:

• Thorough knowledge of electronics system design on an F1 chassis and ability to define accurate 3D layouts and drawings to a high standard.
• Advanced knowledge in schematic design and PCB layout and CAD design packages
• Highly experienced in the production of small batch/prototype analogue and digital circuits.
• Deep understanding of sensor technologies and the support electronics required.
• Proven track record of successful fault investigation, containment, and rectification
• Ability to develop, maintain and deliver project plans for all aspects of electronics and electrical development for an F1 race car and off car support equipment.
• Excellent time management skills and ability to work in a technically challenging environment.
• A sympathetic approach to work colleagues and an ability to integrate within a group environment.
• Capability of working in a multi-national team environment to a very high standard and with a positive can-do attitude.

Equipment and Applications:

• Solid computer knowledge, experience in Microsoft Office products and CAD (e.g. Catia/3DX) and PCB Design (e.g. Altium).
• Knowledge of embedded system programming (e.g. C) and high-level software languages (e.g. C#/VBA) would be advantageous.
• Ability to follow complex regulations related to shipment of materials and facility maintenance requirements.

Work Environment Physical Demands:

An ability to work and prioritize within a high pressure, time sensitive environment while retaining a methodical approach is essential. The role may require some international travel and a willingness to work long and flexible hours including weekends.

This position may require lifting to 50 pounds, repeated bending, squatting and manual dexterity. Fast-paced work environment requiring heavy mental demands. Work environment includes machinery, race cars, 7-post and other rigs, grinding debris, and hazardous fluids. All employees must ensure compliance with the Company Health and Safety Policy, and all relevant other statutory Health and Safety legislation.

_This job description may not detail some less major duties, nor cover duties of a similar nature, which may from time to time be reasonably required by the relevant manager._

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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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