Full-Time
Safety Operations Senior Analyst
Child Safety
xAI
1,001-5,000 employees
Generative AI chatbot leveraging real-time data
No salary listed
Bastrop, TX, USA
In Person
 Legal & Compliance (2) |
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- Expertise in improving Large Language Models (Language Models) specifically related to Cyber Safety and Security Engineering to maximize efficiencies in enforcement and support and ability to propose solutions to increase security and safety of the platform
- Proven expertise in identifying, mitigating, and preventing Child Sexual Abuse Material (CSAM) and Child Sexual Exploitation (CSE), including grooming behaviors and risks in AI-generated content, with strong knowledge of relevant legal obligations (such as National Center for Missing and Exploited Children reporting) and industry standards for protecting minors
- Proven experience in online safety and reducing harm to protect users and preserve Free Speech in the global public square
- Ability to interpret and apply xAI safety policies effectively
- Proficiency in analyzing complex scenarios, with strong skills in ethical reasoning and risk assessment
- Strong ability to utilize resources, guidelines, and frameworks for accurate safety-focused actions and escalations
- Strong communication, interpersonal, analytical, and ethical decision-making skills
- Commitment to continuous improvement of processes to prioritize safety and risk mitigation
- Expertise in data analysis to identify emerging abuse vectors, uncover opportunities for operational efficiencies, and design automations that strengthen enforcement effectiveness and platform safety
- 3+ years of professional experience working in online child safety, criminal investigations, Trust & Safety operations, or a closely related field
- Monitor and take action on content and behavior that goes against our terms of service, escalating as needed
- Have deep expertise in online child safety, including the identification, investigation, and mitigation of a broad range of child safety threats and risks
- Investigate complex cases involving child safety, with the ability to recognize when escalation to internal legal teams and/or external reporting (e.g., NCMEC reports when applicable) is required
- Monitor and take appropriate action on content and user behavior that violates xAI’s terms of service with a primary focus on protecting minors
- Respond effectively to time-sensitive escalations in high-impact, often ambiguous situations
- Process appeals, audit automations, and properly label use cases in the system
- Provide labels, annotations, and inputs on projects involving safety protocols, risk scenarios, and policy compliance
- Support the delivery of high-quality curated data that reinforces xAI’s rules and ethical alignment
- Collaborate with team members to provide feedback on tasks that improve AI's defenses to detect illegal and unethical behavior, as well as align Grok with our rules enforcement
xAI builds Grok, a generative AI chatbot with a Hitchhiker’s Guide-inspired persona that uses real-time data from X to produce current, culturally aware responses. Grok is accessible to premium subscribers on X, via a standalone website, mobile apps, and through an API for developers. The company also develops large-scale AI infrastructure, such as the Colossus supercomputer, to train and run its models. Its goal is to pursue truth-seeking AGI research and grow a capital-intensive platform that could support a major public offering in the future.
Company Size
1,001-5,000
Company Stage
Series E
Total Funding
$42.4B
Headquarters
Palo Alto, California
Founded
2023
Your Connections
People at xAI who can refer or advise you
Simplify's Take
What believers are saying
- Real-time X signals fit trading, sentiment, and breaking-news use cases.[1][6]
- Grok is already being adopted by Gopuff and eToro.[1]
- Musk's ecosystem can bundle X, SpaceX, and Tesla distribution for Grok.
What critics are saying
- Canadian regulators found Grok image generation lacked adequate safeguards.[1]
- Grok's safety failures create litigation, retaliation claims, and employee attrition risk.
- Enterprise buyers can switch away after another public hate-content or deepfake scandal.
What makes xAI unique
- Grok pairs frontier models with native real-time X data access.[6][8]
- xAI markets Grok as truth-seeking, with live citations and search.[6][9]
- xAI offers developer APIs and agentic tools for enterprise workflows.[7][8]
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Growth & Insights and Company News
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↑ 10%xAI, Elon Musk's artificial intelligence company, has reportedly acquired Safe Superintelligence (SSI) in a deal valued at approximately $60 billion. SSI was co-founded in 2024 by Ilya Sutskever, a Pakistani-origin entrepreneur and former OpenAI chief scientist, alongside Daniel Gross and Daniel Levy. The startup focused on developing safe and highly capable AI systems, attracting significant investor interest despite operating with a low public profile. The acquisition is expected to strengthen xAI's position in the competitive AI landscape by combining SSI's AI safety research with xAI's technological ambitions. Industry analysts view the deal as a strategic move that could reshape the balance of power amongst major AI firms, creating one of the most influential AI research organisations globally.
Elon Musk's xAI, SpaceX face class action lawsuit. Elon Musk's companies, xAI and SpaceX, are facing a proposed class-action lawsuit filed by residents in Mississippi who allege that a massive power plant supporting nearby artificial intelligence data centers has created unbearable levels of noise, disrupted their daily lives, and reduced the value of their properties. The case marks another legal challenge tied to the rapid expansion of AI infrastructure and its impact on surrounding communities. The lawsuit was filed in federal court in Oxford, Mississippi, by three residents seeking to represent a class of more than 10,000 people living near the facility. The complaint names xAI, SpaceX, and xAI subsidiary MZX Tech as defendants, alleging that the companies negligently failed to prevent excessive noise generated by the gas-powered turbines that supply electricity to the data centers. Elon Musk himself was not named as a defendant. According to the plaintiffs, the turbines produce constant and intrusive sounds that can be heard day and night. The lawsuit describes the noise as "omnipresent and inescapable," arguing that residents have suffered emotional distress, sleep disturbances, and a loss of enjoyment of their homes. They also claim the continuous noise has negatively affected local property values and transformed peaceful neighborhoods into areas affected by industrial activity. The power facility was developed to support xAI's growing data center operations in Southaven, Mississippi. The project is part of a broader effort to build the large-scale computing infrastructure necessary to power advanced AI systems. As demand for artificial intelligence increases, technology companies are constructing increasingly powerful data centers that require enormous amounts of electricity and cooling resources. The lawsuit argues that the rapid growth of AI infrastructure has created new environmental and quality-of-life concerns for nearby communities. The plaintiffs claim the companies placed the demands of AI development ahead of the rights of local residents by operating equipment that allegedly generates excessive noise around the clock. They accuse the defendants of creating a public nuisance and acting negligently by failing to adequately control the disturbances. The residents are seeking compensation for emotional distress, reduced property values, and other alleged damages. They are also requesting the disgorgement of unspecified profits connected to the operation, arguing that the companies should not financially benefit from activities that allegedly harmed surrounding communities. xAI and SpaceX did not immediately respond to requests for comment regarding the lawsuit. The case comes amid broader legal scrutiny of the companies' AI-related facilities. Earlier in 2026, the NAACP filed a separate lawsuit accusing xAI of violating environmental laws in connection with the same Mississippi facility and its operations. The U.S. Department of Justice later indicated it may intervene in that dispute due to broader legal and policy questions involving AI infrastructure. The lawsuit highlights an emerging legal conflict between the technology industry's race to expand artificial intelligence capabilities and the rights of communities located near large-scale data centers. As AI companies build larger facilities requiring significant power generation, courts may increasingly be asked to determine how much disruption nearby residents must tolerate. Public nuisance claims have historically been used to address activities that interfere with the public's health, safety, comfort, or use of property. In this case, the plaintiffs will need to prove that the alleged noise is unreasonable and that the companies' operations directly caused the harms they describe. The outcome of the case could influence how technology companies design and operate future AI facilities. A ruling favoring residents may encourage stricter noise controls, additional environmental reviews, or greater community engagement before new AI data centers are developed. Although the lawsuit is still in its early stages and no court has determined whether xAI or SpaceX violated the law, the dispute represents a significant legal test over the environmental and community consequences of the expanding artificial intelligence industry. Key legal outcomes. * Mississippi residents filed a proposed class-action lawsuit against xAI, SpaceX, and MZX Tech. * The lawsuit alleges public nuisance and negligence caused by excessive noise from power turbines. * More than 10,000 residents may be included in the proposed class. * Plaintiffs seek damages for emotional distress, lower property values, and other alleged harms. * The case is in its early stages, and no court has ruled on the allegations. Why it matters. * The lawsuit is one of the first major legal challenges involving AI data center noise impacts. * It highlights the environmental and community costs of the AI expansion race. * The outcome could shape future regulations for large-scale AI infrastructure. * The case tests the limits of nuisance law in addressing modern technology projects. * It may influence how technology companies balance innovation with local community concerns. Janice Thompson Janice Thompson enjoys writing about business, constitutional legal matters and the rule of law.
Grok V9-Medium (1.5T) training done: release soon. 6h ago · 0:00 listen · Source: BASENOR - Tesla Accessories Summary. xAI's new AI model, Grok foundation model V9-Medium, has finished its training. This 1.5 trillion parameter model is expected to be released to the public in about two to three weeks. What's interesting is that significant Cursor data was used during its training, with more to come. Cursor is an AI-powered coding assistant, suggesting a focus on developer and coding applications for this Grok version. This new V9-Medium model is three times the size of its predecessor, the V8, which had roughly 0.5 trillion parameters. This jump in scale typically means stronger reasoning and improved performance on complex tasks. Supervised fine-tuning is currently underway, and reinforcement learning will start soon. The bottom line is that stronger coding capabilities could position xAI as a leading developer-first AI platform, potentially enhancing Grok's integration into Tesla vehicles and the X platform. This is an AI-generated audio summary. Always check the original source for complete reporting.
Grok Build runs coding agents at $1 in, $2 out. The catch is that xAI published zero benchmarks. xAI shipped its own terminal coding agent on May 14 and quietly listed the model behind it, grok-build-0.1, in the API on May 20. The rate is $1.00 input and $2.00 output per million tokens, which undercuts OpenAI's Codex model and lands at roughly a seventh of what Claude Opus 4.7 charges on output. The price is the easy part to write about. The hard part is that xAI shipped it with no SWE-Bench number, no coding eval, nothing. You are being asked to route real work to a model on price alone. The short of it. * Grok Build is the CLI (announced May 14, sold through a SuperGrok subscription). grok-build-0.1 is the API model behind it, listed May 20 at $1.00 in, $0.20 cached, $2.00 out, 256K context. * A 500K-input, 80K-output coding task runs $0.66 on it, against $2.00 on OpenAI's Codex model and $4.50 on Claude Code with Opus 4.7. For agents, output price is the whole game. * xAI published no benchmarks. None at all. The cheap rate comes with a real question mark on quality. Grok Build the CLI is not grok-build-0.1 the model. Most of the coverage conflates these, so it is worth pulling them apart before any pricing makes sense. On May 14 xAI announced Grok Build, a command-line coding agent. It plans before it edits, shows clean diffs, runs subagents in parallel git worktrees, has a headless mode for automation, and reads your existing AGENTS.md, MCP servers, hooks, and skills without conversion. If that description sounds like Claude Code or the Codex CLI, that is the point. xAI built it to be a drop-in for developers already using one of those. The CLI itself is distributed through a subscription, not metered per token. Press coverage puts the SuperGrok Heavy tier that includes it around $299 a month, with a discounted intro rate, but those figures come from reporters, not from xAI's own page, so treat them as approximate. Six days later, on May 20, the model that powers it showed up in the pay-as-you-go API as grok-build-0.1. That is the one with a rate card, and the one that matters if you are wiring it into your own agent instead of using xAI's CLI. One odd detail: the model page lists grok-build-0.1 with grok-code-fast-1 aliases, which means it is the evolution of xAI's existing coding line rather than a from-scratch first model. Do not let anyone tell you it is xAI's first coding model. It is the rebrand of one they already had. The rate card next to its rivals. Here is what the model behind each major coding agent costs per million tokens, pulled from each provider's own pricing page. | Model | Input / 1M | Cached / 1M | Output / 1M | Context | | grok-build-0.1 | $1.00 | $0.20 | $2.00 | 256K | | grok-code-fast (predecessor) | $0.20 | - | $1.50 | 256K | | gpt-5.3-codex | $1.75 | $0.175 | $14.00 | 272K | | Claude Sonnet 4.6 | $3.00 | $0.30 | $15.00 | 1M | | Claude Opus 4.7 | $5.00 | $0.50 | $25.00 | 1M | Two things stand out. First, on output, the number that drives coding-agent bills, grok-build-0.1 at $2.00 is 7x under gpt-5.3-codex and 12.5x under Opus 4.7. Second, xAI undercut itself in a confusing way: the older grok-code-fast model it is built on is cheaper on both input and output. If raw cost is all you care about and you do not need whatever grok-build-0.1 added, the predecessor is still the bargain. What a real coding task costs. Rate cards lie about coding agents because the input-to-output ratio is so lopsided. An agent reads a lot and writes less, but the writes are where the expensive output tokens land. Take one concrete task: an agent ingests roughly 500K tokens of context (a medium repo, the relevant files, a few iterations of reading) and produces 80K tokens of output (a feature plus the diffs, tests, and explanation). Here is the bill on each. | Coding agent | Model | Input cost | Output cost | Task total | | Grok Build | grok-build-0.1 | $0.50 | $0.16 | $0.66 | | OpenAI Codex | gpt-5.3-codex | $0.88 | $1.12 | $2.00 | | Claude Code (Sonnet) | Sonnet 4.6 | $1.50 | $1.20 | $2.70 | | Claude Code (Opus) | Opus 4.7 | $2.50 | $2.00 | $4.50 | Grok Build comes in at $0.66. Codex bills three times that for the identical task, Claude Code on Sonnet a bit over four times, and Opus nearly seven. Run a hundred of those tasks a month and you are choosing between a $66 invoice and a $450 one on Opus. That is real money for a team running agents at volume, and it is the entire reason anyone will look at this model despite the missing benchmarks. Caching widens the gap, then narrows it. Coding agents re-send the same context on every turn: the system prompt, the file tree, the files already in view. Prompt caching is what keeps that from bankrupting you. Take a heavier session, say 3M input tokens across forty turns with 70% landing as cache hits, plus 400K output. grok-build-0.1 runs about $2.12, gpt-5.3-codex about $7.54, and Opus 4.7 about $15.55. The output price still dominates, so Grok Build holds its lead. One nuance worth flagging if you are input-bound rather than output-bound: on cache hits alone, gpt-5.3-codex at $0.175 is fractionally cheaper than grok-build-0.1 at $0.20, and Anthropic's 10x cache discount brings Opus reads down to $0.50. xAI's cache discount is shallower (5x, not 10x). So a workload that is almost all cached reads and very little generation closes the gap more than the headline numbers suggest. The further your task tilts toward output, the more Grok Build wins. The number xAI did not publish. This is where the post would normally have a benchmark table. It does not, because xAI has not given TokenCost one. The official announcement has no scores. The model page has no scores. Benchmark trackers list grok-build-0.1 with zero sourced results. For a model whose entire pitch is coding, shipping with no SWE-Bench Verified number is a conspicuous silence. You will see numbers floating around anyway. Ignore them unless they cite xAI directly. The only legitimate coding score in this lineage belongs to grok-code-fast-1, the predecessor grok-build-0.1 is aliased to, and that model sat in the 57 to 70 percent range on SWE-Bench Verified depending on the harness, well short of the high 80s that Opus 4.7 and GPT-5.5 post. If grok-build-0.1 is a meaningful step up from that, xAI has not said by how much. TokenCost has not put grok-build-0.1 through its own harness yet, and that is rather the point: almost nobody has. The early hands-on writeups, like Kilo's teardown, are still feeling out where it breaks. So the honest read is this: a cheap coding model from a lab with a real track record, unproven on any public eval. Cheap and unproven is a fine bet for low-stakes, high-volume work where a wrong answer costs you a retry. It is a bad bet for the autonomous, merge-without-review agent you point at a production codebase. Until xAI publishes, the price tag is the only hard data point you have. Who should actually switch. If you run a fleet of coding agents on bulk, forgiving work, such as mass refactors, test generation, doc updates, or first-draft PRs that a human reviews, the 3x to 7x cost gap is hard to ignore. Wire grok-build-0.1 into a router, send it the cheap-to-retry traffic, and keep your eval harness watching the diff quality. The downside of a bad output is a re-run, and you are paying a fraction per run. If you are shipping autonomous agents against production code, or you need the last several points of SWE-Bench accuracy, stay where you are. Opus 4.7 and gpt-5.3-codex cost more because, on the evidence TokenCost actually have, they earn it on the hardest coding tasks. Grok Build has not shown it can hang there, and finding out the expensive way on a live repo is not worth saving a few dollars per task. To model this against your own input/output mix, the calculator runs the math for any token split, and the pricing page lists every coding model side by side. For the broader xAI rate picture, see the Grok 4.3 Colossus 2 piece, and for the rival agents, the Claude Code vs Codex cost breakdown. Sources. * xAI: Introducing Grok Build - May 14, 2026 CLI announcement, features, distribution * xAI: grok-build-0.1 model page - 256K context, grok-code-fast-1 aliases, above-200K rate note * xAI: API pricing - $1.00 input, $0.20 cached, $2.00 output; Grok 4.3 $1.25/$2.50 * OpenAI: API pricing - gpt-5.3-codex $1.75/$14.00, GPT-5.5 $5/$30 * Anthropic: Claude pricing - Opus 4.7 $5/$25, Sonnet 4.6 $3/$15, 10x cache discount * OpenRouter: grok-build-0.1 - May 20, 2026 API listing date corroboration
Musk's xAI is being sued over its data center generators - now it's buying $2.8B more. 1 hour ago · Elon Musk's xAI has gotten itself in hot water over its use of polluting generators at its data center near Memphis, Tennessee. Now it wants to buy even more of them. In SpaceX's IPO filing, released Wednesday, the company said its xAI division will buy another $2.8 billion worth of turbines for its AI infrastructure over the next three years. One deal, worth $2 billion, is specifically for "mobile gas turbines," the kind that it's currently being sued over. The NAACP filed a lawsuit against xAI last month for operating dozens of unregulated gas turbines that worsen the air quality in one of the most polluted parts of the country. The organization has sought an injunction against xAI's use of the turbines. So far, xAI has been granted permits for 15 turbines. As of a few weeks ago, it was using 46. Each of the types of turbines xAI is operating have the potential to emit more than 2,000 tons of NOx pollution annually, a group of chemicals that contributes to asthma-inducing smog. The company claims that it can operate the turbines for up to a year without permits because they are "mobile" - that is, they're still on the trailer they were shipped on. The company appears to be exploiting a discrepancy between state and federal interpretations. Mississippi claims it doesn't need to permit mobile generators. But federal regulations say that turbines of that size, even if they're on a trailer, are still subject to air-pollution regulations. The EPA ruled earlier this year that xAI was operating the turbines in violation of federal law. SpaceX acknowledges the risks in its IPO filing. "We currently rely significantly on natural gas and gas turbine technology to power our data center operations," it wrote. Injunctions or rescinded permits "would adversely affect our AI business." This editorial summary reflects Tech Crunch and other public reporting on Musk's xAI is being sued over its data center generators - now it's buying $2.8B more.