X

X Development

Moonshot factory developing breakthrough technologies

Head of ML/AI Exploration - Architecture, & Engineering

Full-Time
$307k - $422k+ Bonus + Equity
Expert
PhD
Mountain View, CA, USA
In Person
AI & Machine LearningAI Research

About the job

Requirements
  • A PhD in Computer Science, Artificial Intelligence, or a related technical field.
  • Experience leading a team of research scientists and mentoring and guiding junior researchers or engineers.
  • Experience applying machine learning and artificial intelligence techniques to complex problems.
  • Strong hands-on experience with large language models and transformers, including their theoretical underpinnings and practical application in post-training, such as fine-tuning, reinforcement learning from human feedback, prompting, and agent development.
  • Experience deploying backend large language model applications.
  • Proficiency in Python and deep experience with machine learning and deep learning frameworks such as TensorFlow, PyTorch, JAX, Hugging Face, and LangChain.
Responsibilities
  • Discover, invent, solve, and prototype solutions, including devising experiments, building robust implementations, and designing new algorithms.
  • Lead a small team of research scientists and apply artificial intelligence to important, often ambiguous problems, bridging innovative research and practical solutions.
  • Attract and develop a diverse team of machine learning and artificial intelligence experts, and guide and coach them on their research; foster collaboration, brainstorming, advising, and knowledge sharing.
  • Partner on the technical vision and strategy for early-stage and early-investigation projects, and co-design and co-lead a technical roadmap.
  • Develop, implement, and deploy robust, scalable machine learning and artificial intelligence solutions across multiple projects and scientific domains, focusing on foundation models and transformer architectures.
  • Design and build hybrid artificial intelligence models, potentially integrating large language models with graph-based machine learning, reinforcement learning, and other tools and techniques.
  • Provide research and engineering leadership on early explorations by defining problem scope, identifying optimal machine learning and artificial intelligence approaches, and evaluating their merits.
  • Guide data strategy and contribute to machine learning pipeline development and system evaluation.
  • Contribute to code reviews, establish best practices for machine learning engineering, and optimize code for performance and scalability.
  • Mentor and guide team members, connect the team to the broader research and academic community and other research groups, and integrate the latest advancements.
Desired Qualifications
  • Experience with graph-based machine learning techniques.
  • Meaningful experience or strong understanding of applying artificial intelligence or machine learning in another scientific discipline, such as biology, psychology, or neuroscience.
  • Demonstrable research experience through project contributions and ideally publications in relevant machine learning or natural language processing venues such as NeurIPS, ICML, ICLR, ACL, or EMNLP.
  • Industry experience in a startup or fast-paced research and development environment.

About the company

X The Moonshot Factory is Alphabet/Google’s tech incubator that develops audacious, long-term projects aimed at solving big global problems. It works by exploring hundreds of ideas each year, quickly testing them with early hardware prototypes and multidisciplinary teams, and using a fail-fast approach to weed out unviable concepts. Only a few ideas survive a rigorous vetting process and are turned into official projects; many later become independent companies within Alphabet’s portfolio or spin out as startup ventures funded by Alphabet and external investors. Unlike traditional labs, X focuses on de-risking radical solutions built on breakthrough technologies rather than incremental improvements, with the goal of delivering scalable, real-world impact through new products and companies.

Company Size

1,001-5,000

Company Stage

N/A

Total Funding

N/A

Headquarters

Mountain View, California

Founded

2010

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Simplify Jobs

Simplify's Take

What believers are saying

  • Anori raised $26 million in March 2026 from Prologis, Builders VC, and Series X.
  • Series X Capital launched in August 2026, deploying roughly $500 million into spinouts.
  • Taara operates in over 20 countries after its March 2025 graduation.

What critics are saying

  • Intrinsic moved into Google DeepMind Robotics in February 2026, showing portfolio absorption.
  • X kills many projects before scale, limiting employer stability for long-tenure teams.
  • If spinouts disappoint, Alphabet can shrink X into a talent farm by 2027.

What makes X Development unique

  • X’s August 2026 Series X Capital turns research projects into funded startups faster.
  • Astro Teller’s team graduated Taara in 2025 and Anori in March 2026.
  • X combines hardware prototyping, AI, and Alphabet backing inside one moonshot pipeline.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • 401(k) Retirement Plan
  • 401(k) Company Match
  • Unlimited Paid Time Off
  • Flexible Work Hours
  • Hybrid Work Options
  • Stock Options
  • Company Equity
  • Wellness Program
  • Mental Health Support

Growth & Insights and Company News

Headcount

6 month growth

32%

1 year growth

32%

2 year growth

32%
International Business Times Australia
Aug 13th, 2026
Elon Musk and ex-google X executive warn money could become irrelevant within A decade.

Elon Musk and ex-google X executive warn money could become irrelevant within A decade. Tech leaders debate the future of money in an ai-dominated economy. Published 08/13/26 AT 6:33 PM AEST Elon Musk and a former top Google X executive have separately made strikingly similar predictions in recent weeks: that money, as it currently functions, may become largely irrelevant within the next decade as artificial intelligence and robotics reshape the global economy, though the two men differ sharply on whether that shift represents liberation or a new form of control. Musk made his comments during an interview with Zanny Minton Beddoes, editor-in-chief of The Economist, conducted at his Texas Gigafactory in late July as part of the publication's Insider interview series. "Money won't matter in 2036," Musk told Beddoes, arguing that robots and artificial intelligence will eventually produce far more goods and services than any person could consume. "You want money for food, housing, transport, entertainment," Musk said. "If that is so abundant, what do you need money for in that case?" Musk also predicted the shift would bring deflation rather than inflation, reasoning that if machines keep increasing output while the money supply remains relatively stable, prices should fall over time rather than rise. He separately told Beddoes that he expects artificial intelligence to surpass humanity's collective intelligence within five years, describing the resulting gap in terms he compared to the difference between humans and chimpanzees. "It's hard to imagine that the chimpanzees would be in charge," Musk said, when Beddoes pressed him on how humans would retain control once that gap emerges. Beddoes challenged Musk on the political realities standing between the present and his 2036 timeline, noting that job losses from AI-driven automation tend to arrive well before any offsetting abundance materializes, and that displaced workers historically pressure governments for protection rather than waiting patiently for prices to fall. According to reporting on the interview, Musk acknowledged the transition would likely be turbulent and said battles over income transfers to displaced workers were probably the next major political fight, before moving on to other topics. Musk's comments echo remarks he made previously on the "Moonshots with Peter Diamandis" podcast, where he said people should not bother saving for retirement given his expectation of impending economic abundance. "Don't worry about squirreling money away for retirement in 10 or 20 years," Musk said at the time. "It won't matter." Mo Gawdat, the former chief business officer at Google X and author of the book "Scary Smart," reached a similar conclusion about money's declining relevance in a separate interview, though he framed the shift in far darker terms. Gawdat argued that the current financial system is already largely disconnected from real economic value, describing modern currency as effectively an accounting illusion sustained by fractional reserve banking and concentrated institutional ownership of markets. He predicted that artificial intelligence systems will increasingly take over trading and major financial decisions, eventually consolidating control in ways that benefit a small number of platform owners while leaving most people economically marginalized. Gawdat also forecast that competing AI systems from different companies and countries will effectively converge into what he called a single dominant "AI brain" by 2030, cooperating with one another in ways that transcend national or corporate loyalties. Where Musk framed the coming transition largely in terms of abundance and opportunity, Gawdat warned that a programmable digital currency system, if implemented without strong ethical guardrails, could become what he called "the ultimate form of surveillance" and a mechanism for controlling individual behavior rather than a tool for shared prosperity. Not all economists have embraced Musk's abundance framing. In a Bloomberg opinion piece, columnist Adrian Wooldridge argued that Musk's vision overlooks the role status and relative position play in human behavior, noting that even in a world of material abundance, scarce goods such as desirable locations and social prestige would remain contested, meaning some rationing mechanism resembling money would likely persist regardless of how cheap manufactured goods become. Other economists, including Tyler Cowen and Noah Smith, along with researchers at the American Institute for Economic Research, have raised similar objections, arguing that Musk's post-scarcity vision applies primarily to manufactured physical goods rather than the full range of things people value, and that questions about who owns the robots and how their output gets distributed remain fundamentally political rather than automatic. Musk did not specify in the Economist interview what would replace money once its relevance fades, leaving that portion of his vision comparatively undefined. Gawdat, in his own remarks, offered a more concrete answer, arguing that the only lasting form of value in an AI-dominated economy would be human connection and compassion, concepts he has said motivated his creation of a separate project focused on emotional intelligence within AI systems. Both men's predictions arrive amid a broader wave of commentary from prominent technology figures about the accelerating pace of AI development and its potential economic disruption. While their specific timelines and interpretations diverge, both cite the same underlying driver: a belief that artificial intelligence and robotics are approaching a threshold capable of fundamentally altering how goods, services and decision-making authority are distributed across society within the next 10 to 15 years.

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