Member of Technical Staff - Macrohard at XAI LONDON LTD

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Apr, 26

Salary

440000.0

Posted On

13 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Multimodal Foundation Models, Pre-Training Data Recipes, Data Pipelines, Distributed Training Systems, Eval Harnesses, Monitoring Tools, Reinforcement Learning, Reward Modeling

Industry

technology;Information and Internet

Description

About xAI xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All engineers are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. About the Role Macrohard is building the future of intelligent agents that truly emulate - and surpass - human performance on computers. We're creating computer-use agents capable of mastering any task on a digital desktop: navigating interfaces, executing complex workflows, reasoning step-by-step, and delivering results better, faster, and more reliably than even expert humans. Our team builds and owns the entire agent lifecycle end-to-end: Designing architectures Collecting and curating high-quality interaction data Pre-training foundation models Mid-training, fine-tuning, RL and post-training recipes Build and iterate on high quality evals Deploy and improve the flywheel Who you are Exceptional engineers who own work end-to-end and thrive in meritocratic environments. You're obsessed with quality, rigor, and first-principles thinking — you write clean, efficient code, run tight experiments, and hold yourself (and your teammates) to the highest standards. You have deep expertise or hands-on experience in one or more of: Pushing the boundaries of multimodal foundation models (vision + language + action/computer-use traces) Crafting pre-training and mid-training data recipes — especially for multimodal and agentic data — turning raw interaction traces, synthetic data, and web-scale corpora into high-signal flywheels that unlock breakthrough capabilities Building robust infrastructure and frameworks that power the full agent lifecycle: scalable data pipelines, distributed training systems, eval harnesses, and monitoring tools Applying RL techniques — including online/offline RL, reward modeling, and post-training recipes — to train agents that generalize across complex, long-horizon computer-use tasks reliably Interview Process After submitting your application, the team reviews your CV and statement of exceptional work. If your application passes this stage, the process is: 15-min call with a team member. 3 Technical onsite interviews. 1 meet-the-team interview to present your most exceptional work and have a chance to talk to the team. Offer. Our goal is to finish the main process within one week. Final interviews will be conducted in person. Annual Salary Range $180,000 - $440,000 USD Benefits 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. xAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

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Responsibilities

The role involves building intelligent agents that can master tasks on a digital desktop, including navigating interfaces and executing complex workflows. The team is responsible for the entire agent lifecycle, from designing architectures to deploying and improving systems.