Elon Musk’s xAI offers up to $100/hour for crypto experts after SpaceX mega-merger

Elon Musk’s xAI offers up to $100/hour for crypto experts after SpaceX mega-merger

NEW YORK, Feb 4, 2026, 05:54 (EST)

  • xAI is hiring a remote “Finance Expert – Crypto” to assist in training its AI models, offering U.S. pay between $45 and $100 per hour
  • The hiring surge comes just days after Musk announced SpaceX’s acquisition of xAI, valuing the merged company at $1.25 trillion
  • xAI’s careers page lists several finance specialist roles, including positions in macro research and quantitative trading

Elon Musk’s AI startup xAI is on the hunt for a remote “Finance Expert – Crypto” to assist in training its AI models on digital-asset markets, according to a job posting. The position offers U.S. pay ranging from $45 to $100 an hour. Greenhouse

Musk has folded xAI into SpaceX in a deal that values SpaceX at $1 trillion and xAI at $250 billion, Reuters reported. The move aims to integrate his companies into a unified ecosystem, while competitors like OpenAI, Alphabet’s Google, and Amazon-backed Anthropic continue heavy investments in chips, data centers, and energy. Reuters

xAI’s careers page reveals 294 open positions. Among them are numerous finance roles covering credit, equity research, macro research, and quantitative trading. It suggests an effort to train the model on market mechanics and the language experts use to describe their actions. X

The crypto position requires candidates to deliver “annotations, evaluations, and expert reasoning” spanning text, voice, and video. It also specifies the type of tasks xAI aims for the model to master: “on-chain” analysis (blockchain ledger data), DeFi (decentralized finance platforms that replicate trading and lending without centralized control), derivatives, and cross-venue arbitrage.

The listing specifies the job is fully remote, requiring fixed training hours initially and no visa sponsorship. It also highlights that the company won’t hire for this role in Wyoming or Illinois, and part-time positions won’t include benefits.

Crypto news sites CoinDesk and The Block flagged the job listing that came after the SpaceX-xAI partnership, zeroing in on market microstructure — the nitty-gritty of trading within fragmented venues — and managing risk in nonstop markets. Coindesk Theblock

The job ad quickly made the rounds on X, sparked by a post from Mario Nawfal, as traders and crypto founders dissected the language for clues about Musk’s next moves in AI. X

Markets are already wrestling with what the SpaceX-xAI merger signals for a potential SpaceX IPO. Russ Mould from AJ Bell suggested the deal might “whet the appetite” for what he dubbed the “largest IPO of all time.” Meanwhile, Seraphim Space CEO Mark Boggett described it as the “strongest validation yet” of space’s role as a foundation for the next AI wave, Reuters reported. Reuters

The strategy is complex, with many moving pieces. Reuters reported the merger might face scrutiny over governance and conflicts of interest. SpaceX’s U.S. government contracts add another sensitive dimension. On top of that, crypto market models can collapse quickly when liquidity dries up, rules shift, or the data gets messy.

Ali Javaheri, senior emerging spaces analyst at PitchBook, said Starlink “adds an AI revenue layer” for SpaceX and could serve as a distribution platform for AI services and data. This kind of distribution is crucial if xAI aims to embed its models in products instead of just showcasing them behind a demo screen.

Musk has said power and cooling constraints will force AI computing off the planet. Reuters reported he expects space-based computing to become the cheapest solution within two to three years. He connects this idea to the pursuit of artificial general intelligence — the theoretical stage where machines surpass humans in a wide range of cognitive abilities.

The job posting doesn’t mention any specific product. But the short-term goal is clear: xAI is hiring experts who can break down how crypto traders think about tokens, flows, volatility, and market structure — then convert those insights into training data.

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