AI Engineer Jobs: Where to Find Them and How to Get Hired

The generic software engineer market is contracting while AI engineer jobs multiply. Roughly 86% of employers say they need artificial intelligence built into their products, but most can't find anyone who can do it. AI and machine learning is the single fastest-growing technical capability companies are hiring for, and supply hasn't caught up. An AI engineer who can design AI systems, develop AI solutions, and deploy AI into production competes with almost nobody. A laid-off developer who maintained a CRUD app competes with thousands. That gap is the whole opportunity, and it is why AI engineer jobs pay a premium and the bar to enter keeps dropping for anyone with real skills.

AI engineer jobs span a whole ladder. At the core, the AI engineer builds AI applications on foundation models, wiring up RAG, vector databases, and APIs. A typical listing says the AI engineer will develop and support AI software, design AI solutions, and deploy AI. Above that, the senior AI engineer architects scalable AI systems, and the staff AI engineer owns the hardest systems. A lead AI engineer is responsible for developing the roadmap, leading teams, and driving AI initiatives. A lead AI engineer at Capital One is responsible for developing and deploying machine learning solutions across consumer banking. At the top, a principal AI engineer or AI architect makes model selection calls.

You'll also see the agentic AI engineer and senior agentic AI engineer, a GenAI engineer, an applied AI engineer, and an AI developer. Big companies invent labels: AI senior associate, AI manager, AI sr, GTM AI engineer, ERP AI engineer, and CTIO-AI engineer-sr associate. A senior lead AI engineer may sit over a Gen AI or AI platform team, where the lead AI engineer will develop the shared AI foundations and one AI engineer is responsible for developing each feature. There is heavy overlap with the machine learning engineer and data engineer, but the common thread is the same: take AI capabilities and turn them into something a business pays for.

Read enough ai engineer job postings and the language rhymes. The technical half asks you to design and develop AI solutions and deploy AI into production. They want someone who can build scalable AI, develop and support AI software, support AI software components, and support AI products. They want you to evaluate AI solutions, keep optimizing AI, and own AI capabilities end to end. Listings phrase it as the AI engineer will design, the AI engineer will develop, or the engineer will develop and support AI systems. Senior posts add lead the development and deployment of AI initiatives, designing AI solutions at scale, and applying advanced AI techniques and AI workflows, plus data and analytics engineering and machine learning solutions.

The other half is people and ownership. Companies want engineers who collaborate with cross-functional teams to innovate and enhance customer experiences, mentor team members, and are comfortable leading teams, analyzing complex problems, building client relationships, guiding compliance practices, and helping oversee project performance. A lead AI engineer is responsible for developing both the systems and the people. Whether the post is titled AI engineer jobs, top AI engineer jobs, or artificial intelligence engineer jobs, the core is identical: developing AI, supporting AI products, collaborating across teams, and helping enhance AI across the business. The best AI engineer jobs go to people who can build the AI and drive the initiative around it.

Are AI engineer jobs the same as machine learning jobs? They overlap but aren't identical. A machine learning engineer builds, trains, and deploys machine learning models, working close to the data: feature engineering, model training, and shipping machine learning solutions into production. Machine learning engineer jobs lean on deeper machine learning theory. An AI engineer works one layer up, taking foundation models and turning them into AI applications and AI systems. Where the machine learning engineer trains the model, the AI engineer wires machine learning and large language models into a product and ships the AI software around it.

In practice, plenty of AI engineer jobs ask for both: integrate large language models and also build machine learning models or machine learning solutions for ranking and fraud. At enterprises, one AI engineer or machine learning engineer might own everything from data pipelines to deployed AI systems. If your background is machine learning, that experience transfers directly into artificial intelligence work, and demand for applied artificial intelligence is even hotter than classic machine learning roles. Artificial intelligence is reshaping the job titles, but the underlying machine learning skills still matter, and many AI engineer jobs seat you right next to a data scientist who handles the heavier machine learning modeling.

The companies hiring AI engineers fall into three tiers. Model labs Anthropic, OpenAI, Google DeepMind, and Mistral pay top of market. AI-native scaleups like Scale AI, Hugging Face, Cohere, and Perplexity need people to develop AI solutions fast. Enterprises hold the volume of AI engineer jobs from top companies: Microsoft, Capital One, JPMorgan, and Salesforce use AI technologies to enhance banking services and customer operations. An AI engineer at Capital One might develop and support AI software for fraud, deploy AI into lending, collaborate with cross-functional teams, mentor team members, and lead the deployment of scalable AI.

On pay, a junior AI engineer earns $95,000 to $130,000, mid-level $130,000 to $180,000, senior AI engineer $180,000 to $260,000, and staff or principal $250,000 to $400,000. The rare top roles at the model labs reach $500,000 to $900,000 with equity. The real ceiling is not a salary at all: independent AI consultants charge $300 to $500 an hour. When I opened my own AI consulting company, I signed a client for $50,000 for one week of work in the first week. A job gives you a salary; owning the work gives you a business.

Here's a snapshot of the kinds of AI engineer jobs companies are actively posting right now, across tiers and seniority. It's representative of the market, not a fixed list, but use it to calibrate the roles and salaries to target.

• Senior AI Engineer, Anthropic, Remote (US), $300,000 to $450,000. Build AI systems and agent workflows on frontier models.
• Staff AI Engineer, OpenAI, San Francisco or Remote, $400,000 to $700,000. Lead the development and deployment of AI platform features.
• Lead AI Engineer, Scale AI, Remote, $250,000 to $400,000. Lead teams building machine learning solutions and AI software.
• Agentic AI Engineer, Perplexity, Remote, $200,000 to $320,000. Design AI agents and multi-step AI workflows.
• AI Engineer, Capital One, Remote or Hybrid, $160,000 to $240,000. Develop and support AI software and use AI technologies to enhance banking services.
• Senior AI Engineer, Microsoft, Redmond or Remote, $200,000 to $320,000. Develop AI solutions and deploy AI across products.
• GenAI Engineer, Salesforce, Remote, $180,000 to $280,000. Build generative AI features and AI applications.
• Machine Learning Engineer, Hugging Face, Remote, $200,000 to $350,000. Build and ship machine learning models and AI systems.
• Principal AI Engineer, JPMorgan, New York or Remote, $250,000 to $400,000. Set AI architecture, mentor team members, and guide compliance practices.
• AI Developer, mid-size SaaS startups, Remote, $130,000 to $190,000. Develop AI solutions and integrate large language models.
• GTM AI Engineer, enterprise software, Remote, $170,000 to $250,000. Build AI capabilities and collaborate with cross-functional teams.
• Junior AI Engineer, AI-native startups, Remote, $95,000 to $140,000. Build AI applications and support AI products.

New AI engineer jobs are added daily across LinkedIn, Wellfound, and company career pages. The titles repeat, the salaries cluster in these bands, and the best AI engineer jobs from top companies reward the same thing every time: an engineer who can develop AI solutions, deploy AI, and own the outcome.

Read ten ai engineer job descriptions and the same skills repeat. Python is the backbone. Companies want LLM orchestration, production prompt engineering, RAG and vector databases, agent design, model evaluation, software engineering best practices, and the ability to keep optimizing AI. The agentic stack like OpenClaw, Hermes Agent, and n8n is increasingly expected. You don't need a PhD or deep PyTorch and TensorFlow research; most roles want someone who can develop AI solutions and ship scalable AI products. The fastest proof is two or three real AI projects on GitHub.

Here's the part most developers miss. The best AI engineer jobs are rarely won by applying, because a single posting pulls hundreds of applicants. The engineers who land them get recruited: the hiring manager already knows their name from public work. Build in AI engineering, show it in public, and write about it, and inbound finds you. Even better, the same skills let you skip the job and consult, charging more for a week than most roles pay in a month. Becoming the AI engineer companies chase, employed or independent, is exactly what the AI engineer career path is built to do. Want to become one of these AI engineers? Start there, then come back and pick your role.