How to Find Local Jobs: Best Sites & Resources for Employment Near You
By
Ethan Fahey
•
Dec 29, 2025
Imagine opening a job board in 2025, searching for “machine learning engineer,” and instantly being hit with thousands of listings, with many listings being outdated, mislabeled, or completely irrelevant. That frustration is common, especially as the idea of a “local” job has changed. For AI professionals, local might mean hybrid roles in your city, distributed teams in your time zone, or proximity to an AI hub like Boston, NYC, London, Toronto, or Bengaluru. Since 2020, technical hiring has become more fragmented, with satellite offices, hybrid setups, and global teams reshaping how and where AI work gets done.
The challenge now isn’t a lack of jobs, it’s finding the right ones without wasting hours sorting through noise. That’s where more curated approaches matter. Platforms like Fonzi cut through the clutter by focusing specifically on AI, ML, and LLM roles, matching engineers with companies based on real skills, role requirements, and work preferences rather than keyword-stuffed listings. Instead of endlessly filtering job boards, candidates and recruiters alike get a clearer, faster path to roles that actually fit, making “local” hiring feel intentional again, not overwhelming.
Key Takeaways
The most effective local job search for AI professionals blends niche tech marketplaces (like Fonzi), major job boards with smart filters, and in-person resources like university labs and city meetups in your area.
Companies increasingly use AI in hiring, but not all do it responsibly. Favor platforms that explain how matching works, include human oversight, and actively monitor for bias rather than using opaque algorithms that filter you out without explanation.
Fonzi’s Match Day creates a concentrated hiring window where vetted companies reach out directly to pre-qualified AI talent in specific cities or time zones, reducing the months of waiting typical with cold applications.
The goal of responsible AI in hiring is to reduce noise and surface better matches faster. Human conversations remain central to every hiring decision at curated platforms like Fonzi. If you work in AI or ML, sign up for Fonzi to get matched with local roles that fit your experience and location preferences.
Best Websites for Finding Local Jobs Near You
Local job sites and location filters remain useful starting points, but if you’re an AI specialist, you should prioritize niche, curated platforms over generic boards. The signal-to-noise ratio on general job sites is often brutal, as you’ll wade through hundreds of loosely related postings to find a handful of genuine opportunities that match your skills and location preferences.
That said, major job boards still have their place. LinkedIn Jobs, Indeed, Glassdoor, and Google Jobs all offer robust location filters that let you narrow your search to a specific city or radius. For example, you can search “AI Engineer jobs in Austin, TX” or “Machine Learning Researcher in Toronto, ON” and get results sorted by date, salary, or relevance. These platforms aggregate listings from company career pages and recruiters across the country, giving you broad coverage of what’s available.
To get the most out of these sites, use advanced filters aggressively:
Radius or zip code search to limit results to a commutable area
On-site vs. hybrid vs. remote toggles to match your work preferences
Salary range filters to eliminate roles that don’t meet your compensation expectations
Posted date to surface fresh listings and avoid stale postings
Visa sponsorship if you’re interested in roles that require work authorization support
Beyond the generalist boards, specialized tech job boards can improve your odds. Wellfound (formerly AngelList Talent) focuses on startup roles and lets you filter by city, funding stage, and company size. Levels.fyi’s job board is popular among engineers for its salary transparency and tech-focused listings. University career portals, especially at schools with strong CS or AI programs, often feature local employer partnerships and research-adjacent positions that never make it to Indeed.
Let’s walk through a concrete example. Suppose you’re searching for “LLM engineer in London, UK.”
On LinkedIn Jobs, you enter the title and location, apply the “On-site” filter, and get 200+ results. Many are genuine, but you’ll also see mislabeled “data scientist” roles and listings from recruiters who post the same job multiple times under different titles. The signal is there, but buried.
On Indeed, the same search yields 150 results, but with more duplicates and fewer details about the companies. Salary information is inconsistent, and many listings redirect you to external application pages where you re-enter all your information.
On Wellfound, you search for “LLM” in London and filter by “AI/ML” as the industry. The results are fewer, most having only 30, but almost all are at startups building with large language models, and the company context is richer. You can see funding stage, team size, and even equity ranges.
These sites are solid starting points, but they rarely curate deeply vetted AI roles the way Fonzi and similar curated marketplaces do. If you’re serious about finding high-signal local opportunities, you’ll want to go beyond the generic boards.
Meet Fonzi: A Curated Local Marketplace for AI Engineers & ML Talent
Fonzi is a curated talent marketplace built specifically for AI engineers, ML researchers, infra engineers, and LLM specialists. Unlike generic job boards that accept any employer and any listing, Fonzi vets every company and screens every role for genuine technical depth. The result is a smaller pool of opportunities, but one where nearly every role is high-signal and engineering-heavy.
What makes Fonzi different from the sites you’re used to?
Every company is vetted. Fonzi’s team reviews employers before they can post, ensuring you’re seeing roles at real AI-forward organizations, not recruiters fishing for resumes.
Roles are screened for technical depth. You won’t find mislabeled “AI” roles that are really just business intelligence or Excel work. Fonzi focuses on positions where you’ll actually build, train, deploy, or scale models.
Candidates are pre-vetted. When you create a profile, Fonzi evaluates your portfolio, GitHub, publications, and system design experience. This pre-qualification means employers take your application seriously.
Fonzi regularly features local roles across major AI hubs:
Staff ML Engineer in New York City
Inference Infra Engineer in San Francisco
Applied LLM Scientist in Berlin
ML Platform Engineer in Toronto
Research Scientist in London
Location and time zone are explicitly part of Fonzi’s matching logic. When you set your preferences, whether you want on-site in a specific city, hybrid within a metro area, or remote within certain time zones, Fonzi factors those into every match. This isn’t a checkbox that gets ignored; it’s core to how the platform connects you with employers.
The UI is streamlined. You create one profile, and that profile follows you across all opportunities. No repetitive applications. Clear salary ranges. Explicit details about location expectations, including how many on-site days per week a role requires and which neighborhood or office the team works from.
If you’re an AI professional, create a profile with your accurate city, time zone, work authorization, and relocation preferences. The more complete your profile, the better your local matches.
How Companies Use AI in Hiring and How Fonzi Does It Differently
Since around 2021, many medium and large companies have adopted AI tools to screen resumes, rank applicants, and even schedule interviews. In most cases, candidates never learn that an algorithm played a role in their application’s fate. The process happens silently, behind the scenes, and the decision to move forward or reject arrives without explanation.
Typical AI-in-hiring use cases include:
Resume parsing and keyword scoring. An applicant tracking system (ATS) scans your resume for specific terms. If you wrote “deep learning frameworks” but the job description said “PyTorch,” you might get filtered out, even if you’ve used PyTorch for years.
Basic skill matching. Algorithms compare your listed skills against job requirements and assign a fit score. But these systems often miss nuance: your experience scaling GPU clusters might not register if you didn’t use the exact phrasing the model expects.
Automated rejections. Many companies set thresholds. If your score falls below a cutoff, you receive a form rejection without any human ever reviewing your application.
The risks are real. Candidates from non-traditional backgrounds, such as bootcamp grads, career changers, and self-taught engineers, often get filtered out before a recruiter even sees their resume. Overemphasis on keywords can penalize people who describe their work in slightly different terms. And opaque decision-making leaves AI engineers guessing why they were rejected, with no way to improve.
Fonzi takes a different approach. Yes, Fonzi uses AI, including LLM-based models, to summarize candidate profiles, cluster skill sets, and surface relevant matches. But there’s a critical difference: human review and transparent feedback are built into every step.
Here are three safeguards Fonzi uses:
Bias monitoring. Fonzi’s team regularly audits matching outcomes to check for disparate impact across demographic groups and adjusts models accordingly.
Human-in-the-loop vetting. Every candidate profile and every role is reviewed by a person before it goes live. AI narrows the funnel; humans make the final calls.
Clear visibility into matches. When Fonzi surfaces a role for you, you can see why it’s a match, whether it’s through skills alignment, location fit, experience level, or tech stack overlap. No black boxes.
Fonzi’s AI is designed to reduce noise and speed up discovery, not to replace human recruiters or managers. Human conversations remain central to every hiring decision, and candidates are never ghosted or left wondering what went wrong.
Fonzi Match Day: High-Signal Local Opportunities in a Single Wave
Match Day is one of Fonzi’s signature features. Instead of applying to roles one by one and waiting weeks for responses, Match Day creates a concentrated hiring window where curated companies reach out to a batch of vetted AI candidates all at once.
Here’s how it works:
Candidates update their profiles by a set date. This might be the first Monday of the month or a specific deadline announced in advance. You ensure your skills, experience, location preferences, and availability are current.
Fonzi runs a matching pass. Using its AI and human review process, Fonzi generates a shortlist of candidates for each open role, prioritizing those whose profiles align with the company’s requirements including city, time zone, and on-site expectations.
Companies receive curated candidate slates. Hiring managers and technical recruiters see a prioritized list of candidates who are genuinely interested and eligible for their roles.
Candidates receive high-intent outreach. During Match Day, you may get several messages from hiring managers within a short window, typically 1–3 days. These aren’t generic recruiter blasts; they’re targeted introductions from companies that have already reviewed your profile.
Fonzi sometimes runs location-focused Match Days. Examples include:
Bay Area Match Day for on-site and hybrid roles in San Francisco, Oakland, and the South Bay
NYC–Boston Corridor Match Day for companies and candidates in the Northeast
EU Time Zone Match Day for roles in London, Berlin, Amsterdam, and Paris
This process is less chaotic than traditional recruiting. Instead of spamming dozens of applications and hoping for a response, you update your profile once and let qualified companies come to you. The compressed timeline means decisions move faster, as intros, interviews, and offers can happen within weeks instead of months.
To get the most out of Match Day, time your profile updates ahead of announced events. Opt in to relevant city or region clusters so you’re visible to the right employers.
Comparing Local Job Search Channels for AI Professionals
Not all job search channels perform equally for AI engineers seeking local roles. A quick comparison can save you months of wasted effort—and help you determine which platforms deserve your time.
Channel | Typical Use Case | Signal-to-Noise for AI Roles | Local Focus Strength | Best For |
Generic job boards (Indeed, Glassdoor) | Broad coverage of all industries | Low—many irrelevant or mislabeled listings | Moderate—good radius filters, but noisy results | Early-career exploration, broad market scan |
LinkedIn Jobs | Professional network + job search | Medium—many legit roles, but also recruiter spam | Moderate—can filter by city, but duplicates common | Networking-driven applications, referrals |
State/local government portals (e.g., NYSDOL, WorkForce West Virginia) | Public sector and federal job openings, local workforce programs | Low for AI—mostly non-tech roles, some data positions | High—explicitly local, tied to state workforce | Stability-focused candidates, public safety or government data roles |
University career centers & research labs | Entry-level, internships, research positions | Medium to high—depends on the school’s AI program | High—often city-specific partnerships | New grads, PhD candidates, research-adjacent roles |
Fonzi | Curated AI/ML/LLM roles at vetted companies | High—every role screened for technical depth | High—explicit location/time zone matching | Senior engineers, staff transitions, high-signal job openings |
Generic boards offer breadth, but you’ll spend hours filtering out noise. Government portals like NYSDOL or WorkForce West Virginia are excellent for stability and public safety roles, but they rarely feature cutting-edge AI positions. University career centers are underrated, especially if you’re near a school with a strong AI program, but their reach is limited to local employer partnerships.
Fonzi is purpose-built for AI specialists. If you’re an ML researcher looking to relocate to a new hub, or an infra engineer seeking a hybrid role in your current city, Fonzi’s curated approach means less time wasted on dead-end applications.
Rather than spreading yourself thin across every site, pick 2–3 core channels. Use a generic board for broad awareness, tap into local resources like university labs or workforce programs, and rely on Fonzi for high-signal opportunities that match your location and skills.
Beyond Job Boards: Local Networks, Universities, and Meetups
Many of the highest-signal local AI opportunities are never advertised widely. They’re filled through networks, research labs, and community connections before a job announcement ever goes live. If you’re relying solely on job boards, you’re missing a significant slice of the market.
Universities and research labs are goldmines for local AI talent. Institutions like MIT CSAIL in Cambridge, TUM in Munich, IISc in Bengaluru, and the University of Toronto’s Vector Institute regularly hire engineers and researchers for applied and foundational work. To tap into these opportunities:
Monitor lab job pages directly, as many post before they syndicate to external boards
Attend public talks, seminars, and reading groups hosted by AI labs in your city
Connect with research staff on LinkedIn and express genuine interest in their work
Local meetups and conferences are equally valuable. City-specific ML meetups, MLOps groups, PyData chapters, and hackathons often lead directly to hiring conversations. The people you meet at these events are frequently the same people making hiring decisions at local startups and enterprises. If you’re an applied AI or infra engineer, showing up consistently and contributing to discussions can open doors that no job listing ever will.
Don’t overlook local innovation hubs and AI incubators. Station F in Paris, the Vector Institute ecosystem in Toronto, and the AI-focused co-working spaces in SF’s SoMa neighborhood are all sources of early-stage startup roles. Founders at these hubs often hire through their networks before posting publicly.
When networking, come prepared:
Have a short “local pitch” ready, show who you are, what you build, and what you’re looking for
Follow up with new contacts within 24 hours while the conversation is fresh
Include your Fonzi profile or GitHub link in follow-up messages so people can review your work
Use Fonzi as your structured application backbone while leveraging local events and networks to discover which companies to prioritize. When Match Day comes around, you’ll already have context on the teams reaching out to you.
Practical Tips to Stand Out for Local AI & ML Roles
Even with the right platforms and networks, success depends on how clearly you communicate your skills, impact, and local availability. The AI job market is competitive, so standing out requires more than just listing technologies on your resume.
Tailor your profile to local employers. If you’re targeting roles in a specific city, reference that city explicitly. Mention projects relevant to local industries, such as fintech in London, healthcare AI in Boston, or autonomous vehicles in the Bay Area. Employers want to see that you understand their ecosystem.
Showcase measurable impact. Generic claims like “improved model performance” don’t cut it. Quantify your work with statements such as:
“Reduced inference latency by 40% for a production LLM serving 10M daily requests”
“Scaled GPU training cluster from 8 to 128 nodes, cutting training time from 2 weeks to 3 days”
“Published paper on retrieval-augmented generation at NeurIPS 2023”
Clarify your on-site and hybrid preferences. Vague availability frustrates employers. Specify which cities you’re eligible to work in, how many days per week you’re willing to be on-site, and whether you need visa sponsorship.
Build a strong Fonzi profile. Highlight specific models you’ve built (production LLM applications, recommender systems, computer vision pipelines), infra you’ve scaled (GPU clusters, inference optimization, model serving), and research you’ve published. The more concrete, the better.
Prepare for technical interviews. AI interviews often include:
System design for ML platforms (e.g., “Design a real-time recommendation system at scale”)
Whiteboard sessions on retrieval-augmented generation, fine-tuning strategies, or model evaluation
Discussions of trade-offs in model deployment, observability, and cost optimization
Practice these formats. Review your past projects so you can walk through them in detail. Be ready to edit your approach based on interviewer feedback.
Reference local context in your materials. A line like “Designed production-ready ML systems for fintechs in London” or “Optimized inference pipelines for Bay Area startups working with multi-billion-parameter models” signals that you understand the local landscape.
Keep your profiles up to date monthly, especially before a Fonzi Match Day. Use interview feedback to iteratively refine your positioning. The candidates who land the best roles are the ones who treat job searching as a skill to be improved, not a one-time event.
Use AI to Find Local Jobs Without Losing the Human Element
Local AI hiring has changed fast, and the old playbook of sending applications across generic job boards and waiting months for replies just doesn’t work anymore. For recruiters, it creates noise and a low signal; for AI engineers, it wastes time on roles that aren’t a real fit. Curated platforms like Fonzi cut through that chaos by connecting candidates with vetted companies and high-signal roles that actually match their skills, seniority, and location, whether that means hybrid, same time zone, or a specific AI hub.
Just as important, responsible AI in hiring should make the process clearer and more efficient without stripping away the human element. Fonzi follows a human-in-the-loop approach: AI helps surface the right matches and reduce bias, but real people review profiles, run interviews, and make final decisions. For AI engineers, ML researchers, infra engineers, and LLM specialists, that means fewer irrelevant opportunities and more meaningful conversations. And for recruiters, it means spending time with qualified, motivated candidates instead of sorting through endless resumes.
