As a Member of Technical Staff at xAI, you will build frameworks to improve the reasoning capability, build distributed reinforcement learning systems, techniques for inference time compute (e.g. tree search and planning), and develop environments for agents.

You will get exposure and will be expected to solve and take ownership of components across the entire stack.

Responsibilities

• Build robust and scalable distributed RL systems.
• Optimise frameworks to enable complex inference-time reasoning.
• Develop environments and harnesses for agents.

Basic Qualifications

• Experienced with large-scale reinforcement learning systems.
• Designing and implementing distributed systems.
• Keeping up with state-of-the-art RL and inference time compute algorithms.

Interview Process

After submitting your application, the team reviews your CV and statement of exceptional work. If your application passes this stage, you will be invited to a 15 minute interview (“phone interview”) during which a member of our team will ask some basic questions. If you clear the initial phone interview, you will enter the main process, which consists of four technical interviews:

• Coding assessment in a language of your choice.
• Systems hands-on: Demonstrate practical skills in a live problem-solving session.
• Project deep-dive: Present your past exceptional work to a small audience.
• Meet and greet with the wider team.

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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.

About the Role

We're looking for a Software Engineer to work at the intersection of engineering and research on the Claude Code team. In this role, you'll collaborate directly with Anthropic's researchers to improve Claude’s coding capabilities through tooling, infrastructure, and evaluations. You'll build systems that help us understand where Claude Code excels and where it falls short—and then help close those gaps.

We're looking for engineers who can build robust, complex systems and who thrive in fast-paced, high-intensity environments. You'll take ambiguous problems and turn them into reliable infrastructure that accelerates our research.

Responsibilities

• Design and build eval systems that measure model capabilities across diverse coding tasks

• Build tooling and infrastructure that enables researchers to run experiments at scale

• Develop pipelines for data collection, processing, and analysis

• Create internal tools that improve researcher productivity and accelerate iteration cycles

• Serve as a bridge between product and research—bring strong product intuition to inform which capabilities matter most

• Work closely with researchers to translate research questions into engineering solutions

• Own systems end-to-end—from design through production reliability

You may be a good fit if you:

• Have built and owned complex systems—pipelines, infrastructure, or software that orchestrates many components and handles significant state and logic

• Thrive in high-intensity environments with fast iteration cycles

• Take full ownership of problems and drive them to completion independently

• Are a power user of agentic coding tools and have strong intuition about model capabilities and limitations

• Are comfortable diving into unfamiliar technical domains and figuring things out quickly

• Care deeply about correctness and reliability in the systems you build

• Are excited to work at the boundary between engineering and AI research

• Have at least 5 years of work experience

Strong candidates may also have experience with:

• Writing or maintaining eval/evaluation frameworks

• Reinforcement learning systems

• Working in high-performance, demanding environments—trading firms, quant funds, competitive research labs, or fast-moving startups where intensity is the norm

• Have research computing or scientific infrastructure background

• Have a strong quantitative foundation (math, physics, or related fields)

• Python and TypeScript

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.

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.

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Microsoft AI are looking for a talented Member of Technical Staff, Reinforcement Learning Systems to help build the world's most advanced reinforcement learning systems. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology.

About the Role

We are responsible for designing, developing, and operating the large-scale reinforcement learning systems that power several use cases across the Superintelligence team. We are looking for individuals who can contribute to cutting-edge research and help bridge the gap between cutting-edge research and robust, production-grade distributed systems. The ideal candidate has both distributed systems expertise and a scientific mindset and will be able to build complex and scalable systems from the ground up, identify and resolve performance bottlenecks, debug complex, cross-system issues with extremely high attention to detail, and contribute to solving scientific and research challenges.

Accountabilities

• Develop and tune the pretraining scalable software for Nvidia GB200 72NVL CX8 and AMD MIxxx architectures.
• Benchmark GB200 and AMD MIxxx GPU clusters.
• Gather data and insights to develop the pretraining compute roadmap.

The Candidate we're looking for

Experience:

• Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Technical skills:

• Experience with generative AI.
• Experience with distributed computing.

Personal attributes:

• A high degree of craftsmanship and pay close attention to details.
• Enjoy working in a fast-paced, design-driven, product development cycle.

Benefits

• Software Engineering IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year.
• There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay

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