AI job titles explained
AI job titles are not standard, and the same work can go by three different names. Here is what each one usually means, grouped by the kind of work. Each group links to the live jobs and a fuller guide to the role.
Machine Learning Engineer
Building, training, and shipping machine learning models in production systems.
- Machine Learning Engineer
- Trains models and runs them in production.
- Applied Scientist
- Uses research methods on product problems. A title common at Amazon and Microsoft.
- ML Software Engineer
- A software engineer focused on ML systems, usually more code than modelling.
- Recommendation Systems Engineer
- Builds the ranking models behind feeds, search results and ads.
- Research Engineer
- Builds the code and tools that make research experiments possible at scale.
AI/LLM Engineer
Building applications and infrastructure on top of large language models.
- AI Engineer
- Builds products on top of existing AI models and APIs.
- LLM Engineer
- Specialises in large language models: fine-tuning, serving and retrieval.
- Agent Engineer
- Builds AI systems that plan and take actions with tools, not just answer questions.
- Forward Deployed Engineer
- Works directly with customers to get AI products running in their business.
- Founding AI Engineer
- An early hire at a startup who owns the AI product end to end.
- Solutions Engineer (AI)
- Helps customers design and build with an AI platform, often alongside sales.
Research Scientist
Original research on model architectures, training methods, and capabilities.
- Research Scientist
- Develops new AI methods. Often requires a PhD and publications.
- Member of Technical Staff
- A broad title at AI labs covering research and engineering. Read the description for the real role.
- Research Intern
- A short research placement, usually for PhD or Masters students.
Data Scientist
Statistical modelling, experimentation, and decision science.
- Data Scientist
- Uses statistics and models to answer business questions.
- Decision Scientist
- A data scientist focused on guiding business decisions more than building models.
- Product Data Scientist
- Measures how a product is used and runs its experiments.
- Quantitative Researcher
- Builds mathematical models, mostly in trading and finance.
Data Engineer
Pipelines, warehouses, and the data infrastructure models train on.
- Data Engineer
- Builds the pipelines and storage that data flows through.
- Data Platform Engineer
- Builds the shared data tools other teams rely on.
MLOps / Infrastructure
Training clusters, inference serving, GPU orchestration, and model deployment.
- MLOps Engineer
- Automates how models are deployed, monitored and updated.
- ML Infrastructure Engineer
- Builds the compute and systems that models train and run on.
- Inference Engineer
- Makes trained models respond faster and cost less to run.
- GPU Cluster Engineer
- Runs the large GPU clusters used for training.
Computer Vision
Image, video, and multimodal perception systems.
- Computer Vision Engineer
- Builds systems that understand images and video.
- Perception Engineer
- Helps robots and vehicles understand their surroundings from sensors.
NLP
Natural language understanding, generation, speech, and translation.
- NLP Engineer
- Builds systems that work with text and speech.
- Speech Engineer
- Builds speech recognition and voice models.
Robotics
Embodied AI, control, motion planning, and autonomous systems.
- Robotics Engineer
- Builds the software and systems that make robots work.
- Autonomy Engineer
- Builds the decision making for self-driving vehicles, drones and robots.
AI Safety & Alignment
Alignment research, interpretability, red-teaming, and model policy.
- AI Safety Researcher
- Works on making advanced AI behave as intended.
- Alignment Researcher
- Studies how to keep powerful models aligned with human goals.
- Red Teamer
- Tries to make models fail or misbehave so the problems can be fixed.
- Trust and Safety Engineer
- Builds systems that detect and stop harmful use of a product.
AI Product Manager
Product ownership for AI-powered features and platforms.
- AI Product Manager
- Decides what an AI product does and how success is measured.
- Technical Program Manager (AI)
- Coordinates complex projects across research and engineering teams.
Prompt / Eval Engineer
Prompt design, evaluation harnesses, benchmarks, and model quality measurement.
- Prompt Engineer
- Designs and tests the instructions given to AI models.
- Evaluation Engineer
- Builds the tests that measure how good model output is.
- AI Trainer
- Reviews and improves model answers, often using subject expertise.
Analytics Engineer
Modelling data for analysis - dbt, semantic layers, and metrics.
- Analytics Engineer
- Turns raw data into clean, trusted tables for the business.
- BI Engineer
- Builds the dashboards and reporting models a company runs on.
AI Designer
Interaction and product design for AI-native interfaces.
- AI Designer
- Designs how people interact with AI products.
- Conversation Designer
- Designs how chat and voice assistants talk and behave.
AI-Powered Marketing
Marketing roles where AI tooling is core to the work - content pipelines, AI SEO, automated campaign ops.
- Growth Marketer (AI)
- Uses AI tools to run and test marketing at higher volume.
AI-Powered Sales
Sales roles built around AI tooling - agent-driven outbound, AI research and enrichment, automated sequencing.
- AI Account Executive
- Sells AI products, or sells with AI tools at the centre of the workflow.
AI Growth & Revenue Ops
Revenue and growth operations built on AI workflows: automation, lifecycle orchestration, go-to-market engineering.
- GTM Engineer
- Builds the automation behind sales and marketing teams.
- Revenue Operations Manager
- Runs the tools, data and process behind revenue teams.