AI/ML Engineering Jobs in 2026: Analyzing 10,000+ Posts

Key Findings

• Most roles are mid-level: 78% of AI/ML engineering positions target professionals with 5+ years of experience
• Median salary is $187,500: The middle 80% of roles pay between $122K and $265K annually
• Certifications rarely appear: Just 6% of postings request credentials, with platform-specific certifications like Foundry Data Engineer and cloud ML certifications leading
• Technology firms dominate hiring: Technology (46%), Financial Services (14%) and IT Services (11%) lead postings
• California leads the market: 32% of U.S. roles are posted in California, followed by New York (11%) and Texas (8%)
• Technical degrees increasingly required: Only 30% of junior roles skip degree requirements, staying consistent at 28-33% across all levels

The Role of an AI/ML Engineering Professional

These patterns align with what we see across AI recruitment, where organizations balance hands-on technical execution with cross-functional collaboration.

We categorized each role by seniority and found the market heavily favors mid-level professionals—they account for more than three-quarters of all postings.

We then extracted experience requirements (82% of roles mentioned a specific number) and calculated the average minimum at each level of seniority. Finally, we analyzed job titles to identify the most common naming conventions at each level.

• Junior (19% of roles)
  • Minimum experience: 3 years
  • Common titles: Machine Learning Engineer, AI/ML Engineer, Forward Deployed AI Engineer
• Mid-Level (78% of roles)
  • Minimum experience: 5 years
  • Common titles: Lead AI Engineer, AI Platform Lead Engineer, Computer Vision AI Engineer
• Senior (3% of roles)
  • Minimum experience: 8 years
  • Common titles: Director AI Engineering, Principal AI Engineer, Head of Machine Learning

Most AI/ML engineering jobs are mid-level

What Do AI/ML Engineering Jobs Involve?

So what is an AI/ML engineer actually responsible for day-to-day? We analyzed the language across all job posts to extract the core responsibilities at each level. What emerged is a clear progression of expectations from implementation to vision:

Junior-Level Roles:

• Build LLM and RAG applications with production guardrails and monitoring pipelines
• Develop AI features across computer vision, NLP and generative AI domains
• Implement data pipelines and agent architectures operationalizing models for production

Mid-Level Roles:

• Architect full-stack AI systems from training infrastructure through API integration
• Design scalable MLOps platforms enabling rapid deployment across enterprise environments
• Lead delivery of domain-specific AI solutions combining multiple modalities

Senior-Level Roles:

• Define technical vision and AI strategy driving platform roadmaps to market
• Build and scale AI engineering teams establishing governance and best practices
• Architect enterprise AI ecosystems balancing performance, compliance and business impact

Key takeaway: Junior engineers build and deploy, mid-level engineers architect platforms and lead delivery, senior engineers define vision and scale teams. Each step up means less hands-on coding and more strategic influence over how AI operates at scale.

Who’s Hiring for AI/ML Engineering?

Technology companies lead with 46% of AI/ML engineering postings—nearly half the market. This concentration reflects the discipline’s technical foundations and the rapid AI adoption within tech firms building both internal systems and customer-facing products.

Financial Services follows at 14%, with IT Services rounding out the top three at 11%. Professional Services and Manufacturing each capture 10%, completing the top five.

Technology companies dominate AI/ML engineering hiring

Large companies with 10,001+ employees account for 38% of postings—modestly above the economy’s wider workforce distribution of ~30%. Organizations with 1,001-10,000 employees add another 20%, meaning roughly three out of five AI/ML engineering roles are at companies with over 1,000 employees.

What’s notable is the startup presence—18% of roles come from companies with fewer than 51 employees. This bimodal distribution reflects both massive platform AI investments from tech giants and the proliferation of AI-native startups building specialized vertical solutions.

Companies of all size are hiring for AI/ML engineering roles

Where Are AI/ML Engineering Jobs Located?

California is home to one third of AI/ML engineering opportunities

California dominates with 32% of all AI/ML engineering postings—nearly one in three roles. New York follows at 11%, Texas at 8%, Washington at 7%, and Virginia rounds out the top five at 3%.

Together, California and New York account for 43% of all opportunities, reflecting the concentration of tech companies and financial services firms in these states. The distribution thins quickly beyond the top markets.

Remote roles account for just 13% of postings despite the technical nature of the work. This suggests most organizations prefer AI/ML engineers on-site or hybrid where they can collaborate directly with product teams and data scientists.

California and New York account for 43% of AI/ML engineering jobs

Key takeaway: California offers three times as many opportunities as any other state. New York and Texas provide the next tier, but expect significantly fewer options—and limited remote work—outside these major tech hubs.

Requirements for AI/ML Engineering Jobs

We analyzed the minimum requirements of each job post and found that most AI/ML engineering jobs (68%) require some form of degree. What’s interesting is the consistency across seniority levels—degree requirements remain remarkably stable whether you’re junior, mid-level or senior.

For junior roles, 70% require a degree (55% bachelor’s, 14% master’s, 1% PhD). The remaining 30% don’t specify formal education requirements.

Mid-level positions mirror this pattern: 72% require a degree (56% bachelor’s, 14% master’s, 2% PhD).

Senior roles show virtually identical requirements: 67% require a degree (51% bachelor’s, 12% master’s, 4% PhD).

Degree fields of study that are typically requested of AI/ML engineers include:

• Computer Science (59%)
• Engineering (15%)
• Computer Engineering (11%)
• Electrical Engineering (11%)
• Mathematics (9%)
• Data Science (9%)
• Artificial Intelligence (7%)
• Machine Learning (6%)
• Statistics (7%)
• Information Systems (7%)

Degree requirements are consistent across all levels

Requested Qualifications in AI/ML Engineering Job Posts

AI/ML engineers must excel at machine learning fundamentals and communication. Machine learning appeared in 24% of listings, with communication in 21% and cross-functional collaboration in 19%.

These skills reflect the role’s dual nature—deep technical work building models combined with the ability to translate complex AI capabilities into business value across multiple teams.

Large language models (16%), problem-solving (16%) and deep learning (16%) round out the core technical capabilities, while team leadership (13%) and mentoring (12%) emphasize the collaborative aspects of the role.

Emerging specializations matter equally. Prompt engineering (14%), model evaluation (12%) and natural language processing (12%) increasingly separate AI/ML engineering roles from traditional software development.

Just 6% of postings request specific certifications, but when they do, these credentials lead:

• Foundry Data Engineer
• Foundry Solution Architect
• Foundry Application Developer
• Certified Information Systems Security Professional (CISSP)
• Google Cloud Certified Professional Machine Learning Engineer
• Azure AI Engineer Associate (AI-102)
• AWS Certified DevOps Engineer
• Offensive Security Certified Professional (OSCP)

Key takeaway: Technical depth in machine learning is table stakes, but communication and transformation and change management skills like cross-functional collaboration separate strong candidates from purely technical ones. The shift toward LLMs and generative AI means prompt engineering and RAG architectures are becoming core competencies.

What do AI/ML Engineering Jobs Pay?

More than half (59%) of the AI/ML engineering roles we analyzed included an advertised salary.

There was significant breadth in the ranges employers posted, so we normalized the data by selecting the midpoint for our analysis. From our experience, this is generally a much more indicative number for an employer’s target offer—especially in the current market where initial ranges often run wide.

Across the entire dataset of salaries, we found the median salary for AI/ML engineering positions to be $187,500. The middle 80% of salaries (10th to 90th percentile) ranged from $121,524 to $265,000.

80% of AI/ML engineering salaries fall between $122K and $265K

Breaking AI/ML engineering salaries down by seniority reveals strong progression with notably wide ranges at each level. Junior roles start at a median $150,000, with mid-level positions jumping 29% to $193,000. The progression to senior adds another 24%—reaching a median salary of $240,000.

What’s notable is the breadth: junior salaries vary by $140K from bottom to top, mid-level by $137K, and senior roles show a $147K range. This wide distribution reflects variance in company size, domain specialization and geographic concentration—a senior ML engineer at a Series A startup in Austin faces very different compensation than one at a FAANG company in San Francisco.

The overlap between tiers is substantial. Junior roles at the 90th percentile ($244,000) exceed mid-level roles at the 10th percentile ($127,550) by over $116,000, suggesting that specialized expertise or high-demand domains can compress traditional seniority bands.

AI/ML engineering salaries jump 29% from junior to mid-level

Key takeaway: AI/ML engineering positions pay exceptionally well. The median senior-level salary of $240,000 puts these roles in the top 4% of all earners in the United States. Even junior engineers earning the median $150,000 land in the top 12%.

Final Thoughts

For Candidates: Build hands-on experience with LLMs, RAG architectures and production MLOps early—these capabilities appear across all levels. For mid-level roles, demonstrating you’ve architected full-stack AI systems and led cross-functional delivery separates candidates. At senior levels, experience defining technical vision and scaling engineering teams matters more than coding prowess alone. Cloud platform certifications help, but communication and mentoring skills are increasingly table stakes.

For Employers: The tight salary clustering around $193,000 for mid-level roles reflects market maturity—fall significantly below that and expect longer time-to-fill. The strongest signal for senior candidates is experience building organizational AI capabilities and establishing governance frameworks, not just shipping individual models. Remote flexibility remains surprisingly limited in this field—expect candidates to push for hybrid arrangements despite the technical nature of the work.

Methodology

We analyzed 10,133 AI/ML engineering job postings collected from LinkedIn, Indeed and Glassdoor between November 2024 and January 2025. The dataset was limited to full-time roles posted in the United States that explicitly mentioned “AI engineering,” “ML engineering,” “machine learning engineer” or close variations in the job title.

Duplicate postings were removed using job title, company name and location matching. Seniority levels were determined by analyzing job titles alongside minimum experience requirements stated in each posting. When experience ranges were provided, the lower bound was used for consistency.

Salary data was extracted from the 59% of postings that included compensation ranges. We used the midpoint of each range for analysis, as this most closely reflects employer target offers in practice.

Industry classifications were assigned based on company descriptions and verified against LinkedIn company data where available. Geographic analysis was conducted at the state level using the primary job location listed in each posting.