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LinkedIn’s New AI Tools Rethink How Job Seekers Discover Career Paths
By understanding intent, not just keywords, LinkedIn’s AI helps users find more meaningful job matches — even in roles they hadn’t considered.
LinkedIn is rolling out new AI-powered job search tools designed to match users with better career opportunities — whether or not they know exactly what they’re looking for.
The new features allow job seekers to describe their goals in natural language instead of relying on rigid keywords or exact job titles. Want to explore “business development in the video game industry” or “use your marketing skills to help fund cancer research”? LinkedIn’s AI now understands even the most abstract of intents and can recommend roles that align — even if job listings don’t use those specific terms.
“It’s about helping people express what they want to do in their own words,” said Rohan Rajiv, head of career products at LinkedIn. “Our AI figures out the semantics behind that and matches it with relevant opportunities”.
This evolution in job search is powered by large language models (LLMs), the same kind of AI that fuels tools like ChatGPT. These models excel at inferring meaning from both job descriptions and user profiles, identifying matches that traditional search engines might miss. For instance, the AI can assume a web developer is proficient in HTML, even if they haven’t explicitly listed it on their profile.
Another tool, called Job Match, assesses how well a user fits a role based on their skills and experience. It also pinpoints gaps — like missing certifications or limited exposure to a required technology — helping users better prepare before they apply.
LinkedIn’s AI also offers context around job listings, such as whether an employer is actively hiring, whether a role is promoted on the platform, and typical wait times between application and response.
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Erran Berger, LinkedIn’s VP of engineering, explained that recruiters are also overwhelmed: “They’re spending up to five hours a day filtering through applications, and over half don’t meet basic requirements”. The new AI features aim to improve quality on both sides, helping candidates apply more strategically and saving time for hiring teams.
With over 15 million job postings and 11,000 job applications submitted every minute on LinkedIn, these tools are a major step toward transforming a job market that often feels inefficient and transactional.
CEO Ryan Roslansky framed the update as part of LinkedIn’s long-term evolution: “Twenty years ago, we connected recruiters with passive candidates. Today, we’re using AI to redefine what’s possible in job discovery”.
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NVIDIA Puts GPT-5.5 Codex In Hands Of 10,000 Staff
The chipmaker has significantly expanded OpenAI’s latest model across teams from engineering to HR under tight internal controls.
NVIDIA has started rolling out OpenAI’s GPT-5.5 model through the Codex coding agent to more than 10,000 employees, extending the tool well beyond software teams and into core business functions.
The deployment covers engineering, product, legal, marketing, finance, sales, HR, operations and developer programs. Staff are using Codex for coding, internal research and routine knowledge work as companies test whether AI agents can move from demos to daily use.
GPT-5.5 is running on NVIDIA’s GB200 NVL72 rack-scale systems, linking OpenAI’s newest model directly to the chipmaker’s latest infrastructure push. NVIDIA said the systems cut cost per million tokens by 35 times and raise token output per second per megawatt by 50 times versus earlier generations.

Inside the company, it says the effects are immediate. Debugging work that once took days is being finished in hours and experiments across large codebases that used to stretch over weeks are now handled overnight. Teams are also building features from natural-language prompts with fewer failed runs.
In a company-wide note urging staff to adopt the tool, CEO Jensen Huang wrote: “Let’s jump to lightspeed. Welcome to the age of AI.”
Security remains central to the rollout. Codex can connect through Secure Shell to approved cloud virtual machines, allowing agents to work with company data without moving it outside approved environments. NVIDIA said it assigned cloud VMs to employees so agents run in isolated sandboxes with full audit trails.
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The company added that the setup uses a zero-data-retention policy. Access to production systems is read-only through command-line tools and internal automation layers.
The move also highlights NVIDIA’s long relationship with OpenAI. NVIDIA said the partnership began in 2016, when Huang personally delivered the first DGX-1 AI supercomputer to OpenAI’s San Francisco office.
The two companies have since worked across hardware and model deployment. NVIDIA also said OpenAI plans to deploy more than 10 gigawatts of NVIDIA systems for future AI infrastructure.
For Gulf markets pouring money into sovereign AI and enterprise automation, the signal is clear: internal AI agents are moving from pilot phase to standard tooling.
