Member of Technical Staff - RL Infrastructure

Benefits

Parental leave

Not verified

Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

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Childcare support

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Learning budget

Not verified

Verification

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Salary

$180K-$440K not verified - source not recorded; timestamp not recorded

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

166% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Member of Technical Staff - RL Infrastructure Palo Alto, CA 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 employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. ABOUT THE ROLE: xAI is seeking experienced software engineers to create robust data pipelines, comprehensive evaluations for benchmarking LLMs, and automation frameworks to increase the productivity of researchers and engineers. Typical problems you will deal with include the following: - We have a new agentic model capability that we'd like to improve. How do we design an efficient and robust environment for the agent to perform actions in? - Evaluations and observability are a core part of knowing what we need to improve in our models. What new features can we add into our evaluation framework to ease the workflow of researchers & engineers and increase observability? - A new open-source evaluation dataset has been released and researchers would like to track our models performance on it. How should

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