Base salary only. Updated for 2026.
Applied AI and ML engineering is the most competitive talent market in engineering right now. Tens of thousands of open ML engineer positions in the U.S., and the vast majority target 5+ years of experience. The average ML engineer base salary falls in the $162,000-$183,000 range depending on source, but that number hides massive variance. A mid-level ML engineer building predictive quality models for additive manufacturing earns $120,000-$155,000 base in Boston. A staff ML scientist developing physics simulation for autonomous vehicles earns $250,000-$300,000 base in the Bay Area. That is a 2x spread at the same job title. What matters is what you work on, not what your LinkedIn headline says.
Mid-level ML engineers (3-5 years) earn $120,000-$145,000 base. These are engineers applying ML to specific domain problems rather than building foundational models. A mid-level ML engineer developing in-situ defect detection for metal additive manufacturing earns $120,000-$155,000 base in Boston.
Senior ML engineers (5-10 years) earn $140,000-$220,000 base. The range depends on domain and location. Predictive maintenance engineers building models for large wind fleets spanning thousands of turbines earn $140,000-$180,000 remote from Denver. Edge AI engineers deploying quantized inspection models to NVIDIA Jetson hardware at factory installations pull $175,000-$220,000 in the Bay Area. On the contract side, ML engineers optimizing autonomous mining fleet dispatch earn $166,000-$198,000 remote.
Principal ML engineers ($195,000-$225,000 base) lead technical direction for an AI practice area. A principal digital twin engineer combining physics-based semiconductor process models with real-time sensor data earns $190,000-$230,000 in Austin.
Staff ML scientists ($250,000-$300,000 base) define research direction and make cross-cutting architecture decisions. The top of the range is a staff ML scientist working on sim-to-real transfer for autonomous vehicle simulation at $250,000-$300,000 base in the Bay Area.
San Francisco Bay Area is the highest-paying market for ML engineers. Senior roles run $175,000-$220,000 base. Staff roles reach $250,000-$300,000 base. The density of autonomous vehicle companies, AI startups, and semiconductor equipment firms all competing for ML talent keeps the floor high.
New York City senior ML engineers earn $170,000-$215,000 base. Fintech, building technology, and applied ML companies drive the demand. Remote-eligible roles based in NYC add to the addressable market.
Seattle senior ML engineers earn $170,000-$210,000 base. The hyperscale cloud operators headquartered here run large internal ML platform teams and pay accordingly.
Austin is growing rapidly as an ML hub. Semiconductor companies, EV manufacturers, and defense tech startups all need applied ML talent. Principal roles at semiconductor manufacturers reach $190,000-$230,000 base.
Denver serves as a base for ML engineers working on energy, mining, and aerospace applications. Senior remote roles from Denver pay $140,000-$198,000 base. The cost of living advantage makes Denver competitive with coastal markets on a purchasing power basis.
LLM and generative AI specialists command a 30-50% premium over baseline ML salaries. That premium is baked into the base salary data above for roles involving GenAI work, but it warrants separate attention because it is the single largest comp driver in the ML market.
RAG engineers, LLM fine-tuning specialists, and AI infrastructure engineers who build the serving and orchestration layer for production LLM deployments are in a market where demand outstrips supply by a wide margin. Two years ago, these roles barely existed. Now every company deploying LLMs in production needs engineers who can manage the full serving stack.
For TechElites listings, the applied AI roles focus on ML engineers who work with physical systems. Predictive maintenance, autonomous systems simulation, edge AI deployment, digital twins. The GenAI premium shows up in roles that bridge foundation models with physical world applications: using reinforcement learning to control building HVAC systems, simulating sensor data for autonomous vehicles, generating process optimization strategies for semiconductor manufacturing.
Domain expertise is the single biggest differentiator in applied AI comp. An ML engineer who can write PyTorch code is common. An ML engineer who can write PyTorch code and understands wind turbine SCADA data, semiconductor etch processes, or autonomous haul truck dispatch optimization is rare.
The premium for physical-world ML keeps growing because these roles resist outsourcing and bootcamp pipelines. Building a defect detection model that runs at 300 parts per minute on embedded hardware with less than 0.1% false reject rate requires years of domain knowledge. Same for building digital twins of semiconductor manufacturing processes that predict yield impact before committing silicon.
Sim-to-real transfer is a specific competency commanding top comp. Closing the gap between simulated performance and real-world results for autonomous vehicles, robots, and industrial systems requires understanding of physics modeling, domain randomization, and sensor simulation. It sits at the intersection of ML research and physical engineering, which is exactly where the talent pool is thinnest.
A wind energy company operating a large fleet of turbines across dozens of wind farms in North America.
Semiconductor manufacturer with major US fabrication operations, investing heavily in digital transformation of process engineering.
Machine vision company building AI-powered inspection systems for electronics and automotive manufacturing. The systems inspect millions of parts per month across dozens of factory installations.
Self-driving company building the simulation platform that validates autonomous vehicle behavior across billions of miles of simulated driving.
Building technology company that uses ML to reduce energy consumption in commercial buildings. The platform manages HVAC systems in hundreds of buildings, reducing energy use by an average of 22%.
Senior ML engineers earn $140,000-$220,000 base salary in 2026. Staff-level ML scientists reach $250,000-$300,000 base. The average ML engineer base salary falls between $162,000 and $183,000, but the actual range spans from $120,000 at mid-level to $300,000+ at staff level, depending on specialization, domain, and location.
Autonomous systems simulation and sim-to-real transfer is the highest-paying specialization in the applied ML data, with staff scientists earning $250,000-$300,000 base in the Bay Area. LLM and GenAI specialists command a 30-50% premium over baseline ML salaries. Among physical-world ML roles, digital twin engineering for semiconductor manufacturing reaches $190,000-$230,000 base at the principal level.
ML engineers at pure AI companies tend to have higher total comp due to equity, but base salary for applied ML roles in manufacturing, energy, and autonomous systems ($140,000-$220,000 senior) is competitive with AI product companies. The advantage of applied ML roles is that the domain expertise creates a moat: fewer candidates can compete for a role requiring understanding of semiconductor etch processes and ML, which keeps base salary high.
A PhD is required or strongly preferred for staff and principal ML roles paying $195,000+ base. The staff ML scientist role at $250,000-$300,000 base requires a PhD. At the senior level ($140,000-$220,000), production experience deploying ML models to physical systems can substitute for a PhD. Mid-level roles ($120,000-$155,000) typically require an MS with 3-5 years of applied experience.
See applied ai for physical systems roles with comp on every listing.
