AI/ML Engineer
Design, build, train, and deploy machine learning models and AI systems that power real-world products - from recommendation engines to large language model integrations.
Mid-Senior$200K - $385K
Explore GenAI Career Paths
Explore AI careers and discover what skills you need to break in or level up.
Three families of AI work
"Working in AI" has stopped meaning one thing. Every role below falls into one of three groups, and knowing which one you are aiming at matters more than the job title.
Engineering
Build and run the systems. Retrieval, serving, cost, reliability.
LLM engineers · Generative AI developers · MLOps · Infrastructure
Product & delivery
Decide what gets built, and make it survive contact with real customers.
Solutions architects · Forward deployed engineers · AI product managers
Judgement
Define what good output actually looks like, and hold the system to it.
Prompt engineers · AI trainers · Ethics & governance specialists
Pick a role to explore
Open a role for its responsibilities, required skills, learning roadmap, and the interview problems that come up for it.
LLM Engineer
Design, fine-tune, and integrate large language models into production applications using the latest foundational models and orchestration frameworks.
Mid-Senior$110K - $215K
Explore Prompt Engineer
Design, test, and optimize prompts to maximize LLM performance across products and pipelines.
Mid$90k - $160k
Explore Spotlight Role
Forward Deployed Engineer
Embed directly at customer organizations to design, build, and deploy production-grade AI systems from scratch - serving simultaneously as engineer, architect, and strategic advisor.
Explore
Generative AI Developer
Build and ship production AI applications using generative models - from text and image generation to multimodal and agentic systems.
Mid$125k - $210k
Explore MLOps Engineer
Build and operate the infrastructure, pipelines, and tooling that enable ML models to go from experiment to production reliably and at scale.
Mid-Senior$120K - $200K
Explore AI Solutions Architect
Design end-to-end AI system architectures that align business requirements with the right models, infrastructure, and integration patterns.
Senior$165K - $306K
Explore Data Scientist
Extract actionable insights from complex datasets using statistical modeling, machine learning, and AI techniques to drive business decisions.
Mid$125K - $251K
Explore AI Product Manager
Define AI product vision, roadmap, and success metrics for ML-powered features.
Mid-Senior$163K - $337K
Explore AI Ethics & Governance Analyst
Identify, assess, and mitigate ethical risks in AI systems - from bias and fairness to transparency, privacy, and regulatory compliance.
Mid$85k - $140k
Explore AI Trainer / RLHF Specialist
Shape AI model behavior through data annotation, human feedback collection, and reinforcement learning from human feedback (RLHF) processes.
Entry-Mid$80k - $140k
Explore AI Copilot Engineer
Build AI assistants embedded inside enterprise software platforms to automate workflows, surface insights, and enable natural language interaction with complex business systems.
Mid$110K - $215K
Explore Multi-Agent Systems Engineer
Architect and build systems where multiple specialized AI agents collaborate, delegate subtasks, and hand off work to accomplish complex multi-step goals autonomously.
Mid-Senior$179K median
Explore AI Reliability Engineer
Monitor, maintain, and improve the reliability, performance, and safety of AI systems in production - specializing in AI-specific failure modes like hallucination, drift, and latency degradation.
Mid-Senior$160K - $272K
Explore AI Infrastructure Optimizer
Reduce the cost and latency of AI systems through model compression, quantization, caching, and infrastructure tuning - making production AI faster and more affordable at scale.
Senior$156K - $329K
Explore Where a role is covered by Levels.fyi the badge shows the middle half of reported US total compensation (25th-75th percentile, base plus stock and bonus); the rest are our own 2026 estimates. Neither is an offer. Real pay varies widely with location, company stage, and equity - a senior role at an early-stage startup and the same title at a large enterprise can sit at opposite ends of a band. Treat these as a way to compare roles against each other, not as a number to expect.
How to read these guides
Read the responsibilities, not the title
Two companies can advertise an "AI Engineer" and mean completely different jobs. The duties section of each guide is the honest comparison; the label is not.
Compare against the job you have now
Every guide opens with a table putting the role beside the one people most often arrive from, so you can see what carries over and what actually changes.
Read the spread, not the midpoint
A wide gap between the 25th and 75th percentile usually means the title covers several genuinely different jobs. Worth knowing before you negotiate.
Most people arrive sideways
Most of these are not new disciplines so much as existing ones with a probabilistic component dropped into the middle. That is why the fastest route in is almost always sideways: a backend engineer becomes an LLM engineer, an SRE becomes an AI reliability engineer, a domain expert becomes a prompt engineer. The transferable part is your existing craft. The new part is evaluation - knowing whether the output is any good, and designing for the times it will not be.
Compare AI roles side by side
Every role, its seniority, the skills it leans on, and the role people most often move across from.
| Role | Level | Core skills | Transitions from |
|---|---|---|---|
| AI/ML Engineer | Mid-Senior | Python, PyTorch / TensorFlow, MLOps | Backend Engineer · Data Engineer · Data Scientist |
| LLM Engineer | Mid-Senior | Python, RAG & embeddings, Evaluation | Backend Engineer · AI/ML Engineer · Generative AI Developer |
| Prompt Engineer | Mid | Prompt design, Evaluation, Domain depth | Technical Writer · Domain Expert / Analyst · QA Engineer |
| Forward Deployed Engineer | Senior | Full-stack, Customer delivery, Model APIs | Full-Stack Engineer · Solutions Engineer · AI Solutions Architect |
| Generative AI Developer | Mid | App development, Model APIs, Streaming UX | Frontend Engineer · Full-Stack Engineer · Mobile Developer |
| MLOps Engineer | Mid-Senior | Kubernetes, CI/CD, Monitoring | DevOps Engineer · Site Reliability Engineer · Data Engineer |
| AI Solutions Architect | Senior | System design, Cloud platforms, Stakeholder comms | Solutions Architect · Senior Backend Engineer · Forward Deployed Engineer |
| Data Scientist | Mid | Statistics, SQL, Experimentation | Data Analyst · BI Analyst · Statistician |
| AI Product Manager | Mid-Senior | Product discovery, Evaluation, Model literacy | Product Manager · Technical Program Manager · Business Analyst |
| AI Ethics & Governance Analyst | Mid | Policy & risk, Model auditing, Documentation | Risk & Compliance Analyst · Policy Advisor · Data Governance Lead |
| AI Trainer / RLHF Specialist | Entry-Mid | Writing, Domain judgement, Annotation | Technical Writer · Subject-Matter Expert · Editor / Teacher |
| AI Copilot Engineer | Mid | Developer tooling, Agent frameworks, IDE extensions | Developer Tools Engineer · IDE / Plugin Developer · LLM Engineer |
| Multi-Agent Systems Engineer | Mid-Senior | Distributed systems, Orchestration, Tool calling | Distributed Systems Engineer · Backend Engineer · LLM Engineer |
| AI Reliability Engineer | Mid-Senior | SRE practice, Observability, Failure analysis | Site Reliability Engineer · Platform Engineer · MLOps Engineer |
| AI Infrastructure Optimizer | Senior | GPU serving, Performance tuning, Cost modelling | Performance Engineer · Cloud Infrastructure Engineer · MLOps Engineer |
Frequently asked questions
Which AI career pays the most?
AI solutions architect and forward deployed engineer sit at the top of the bands listed here, both because they are senior by definition and because they combine engineering depth with direct customer and revenue exposure. Infrastructure and MLOps roles follow closely, since GPU cost and reliability translate straight into money saved. Pay tracks seniority and business proximity far more than it tracks which corner of AI you work in.
Do I need a PhD to work in AI?
For research scientist positions, usually yes. For almost every role on this page, no. LLM engineering, generative AI development, MLOps, solutions architecture and AI product management are engineering and product jobs first. They reward people who can build something that works, measure whether it is actually good, and keep it running affordably.
Can I move into AI from software engineering?
It is the most common route, and the shortest. Most of these roles are software engineering jobs with a probabilistic component in the middle, so your existing skills transfer almost entirely. The gap is usually not coding ability but evaluation: knowing how to tell whether a model output is good, and designing systems that stay useful when it is not.
Which AI career is easiest to break into?
AI trainer / RLHF specialist has the lowest technical barrier, since it rewards writing quality and subject-matter judgement over engineering. Prompt engineering is next if you already have deep expertise in a domain. Both are legitimate entry points, and both are far easier to enter with a portfolio of concrete work than with a certificate.
Do I need strong maths for these roles?
For research and for core data science, yes - statistics especially. For the applied engineering roles, you need to understand what a model is doing well enough to debug it, but you will spend far more time on retrieval quality, latency, cost and evaluation than on derivations.
How should I prepare for AI interviews?
Work the problems tagged to your target role. Every career guide on this site lists the coding challenges and system design problems that come up for that specific role, each with a full solution and the follow-up questions interviewers press on.