Find your next AI Engineering role
2+ open roles · 365+ companies hiring
2 open positions
Research Scientist, Agent Robustness
Scale AI
Scale Labs is launching a new team focused on policy research to bridge the gap between AI research and global policymakers, enabling informed, scientific decisions about AI risks and capabilities. This team tackles fundamental challenges in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. The role involves collaborating broadly across industry, the public sector, and academia, and regularly publishing findings to shape the future of AI safety and alignment.
$216k - $270k
Research Engineer, Safety
decagon
Decagon is seeking a Research Engineer focused on Safety to ensure the reliability and controllability of their AI agents. This role involves identifying real-world failure modes and developing the necessary models, evaluations, and safeguards to prevent them. The ideal candidate will be a strong engineer passionate about advancing applied AI safety in production, with the ability to own projects end-to-end and make high-impact technical decisions.
$200k - $400k
Frequently asked questions
What counts as an AI Engineering job?
AI Job Board lists engineering roles that build, deploy, or operate AI/ML systems — including LLM engineering, RAG (retrieval-augmented generation), AI agents, prompt engineering, AI infrastructure and MLOps, model serving/inference, and fine-tuning. It excludes non-engineering functions like AI sales or marketing roles.
What is the difference between an AI Engineer and a Machine Learning Engineer?
An AI Engineer typically builds applications on top of existing models — LLM integration, RAG pipelines, agent orchestration, and prompt design. A Machine Learning Engineer more often trains, fine-tunes, or productionizes custom models. Many companies use the titles interchangeably, so search both when browsing.
Does AI Job Board include AI infrastructure and MLOps roles?
Yes. Titles such as AI Infrastructure Engineer, ML Platform Engineer, GPU Infrastructure Engineer, Inference Engineer, MLOps Engineer, and AI Site Reliability Engineer are all covered — these roles focus on the systems that serve and scale AI models rather than building models themselves.
Are remote AI and ML jobs available?
Yes. Filter by "Remote" on the jobs page to see fully remote AI/ML engineering roles, or browse the remote jobs feed directly.
What skills are most in demand for AI engineering roles?
The most commonly requested skills are LLM APIs (OpenAI, Anthropic, Gemini), RAG and vector databases (Pinecone, Weaviate, Qdrant), agent frameworks (LangGraph, CrewAI, AutoGen), inference/serving tools (vLLM, Ray Serve, TensorRT-LLM), and fine-tuning. Use the skill filters on the jobs page to browse by specific technology.
How fresh are the job listings?
Listings are sourced continuously from company career pages and refreshed automatically. Each job shows when it was posted, and the sitemap and API expose last-updated timestamps for every posting.
How much do AI Engineers get paid?
Compensation varies widely by role, seniority, and location. Where employers disclose a salary range, it is shown directly on the job listing — filter by salary range on the jobs page to narrow results to your target compensation.