Find your next AI Engineering role
2+ open roles · 71+ companies hiring
2 open positions
Head of Policy & Security Research Lab
Scale AI
Scale Labs is seeking a highly experienced, strategic, and mission-driven leader to drive the policy research priorities of the organization. This is a unique opportunity to lead a team of research scientists, policy experts, and engineers focused on foundational AI safety and security work. The role involves owning the day-to-day strategy, direction, and execution of Scale's Policy Research Lab, collaborating with internal researchers and leading labs across governments, industry, and academia. You will drive initiatives related to frameworks and benchmarks for frontier AI models, stay engaged with policy and research communities to set trends, and manage the lab's projects, partnerships, and strategy, including direct work with global AI safety institutes. A key part of the role is finalizing, coordinating, and executing the lab's recruiting plan and research agenda.
$178k - $247k
Strategic Projects Lead, Red Team
Scale AI
Scale's Red Team and Safety function is responsible for stress-testing the world's most capable AI models and influencing how labs, governments, and enterprises deploy them. This role focuses on owning day-to-day partnerships with frontier model developers, requiring technical curiosity, comfort with researchers, and operational rigor. You will collaborate closely with research, operations, and go-to-market teams to manage a portfolio of partnerships, acting as a subject-matter expert in customer conversations and coordinating delivery with internal teams.
$122k - $190k
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.