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745 open roles at 309 companies, from top AI labs and YC startups. Refreshed every day.

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745 open positions

Lead Software Engineer - Full Stack/AI Engineering Focus

11d ago
J

JPMorganChase

As a Lead Software Engineer at JPMorganChase, you will be an integral part of an agile team focused on enhancing, building, and delivering trusted, market-leading technology products in a secure, stable, and scalable manner. You will be a core technical contributor responsible for critical technology solutions across multiple technical areas within various business functions to support the firm's business objectives. This role involves designing, developing, and delivering scalable, secure, and resilient full-stack solutions, owning the end-to-end delivery lifecycle for strategic platform capabilities, and leading the design and development of cloud-native, containerized microservices. You will also drive technical architecture and design decisions, build high-performance backend services and APIs, develop modern operational and administrative user interfaces, and design and implement event-driven and streaming architectures. Additionally, you will champion engineering excellence through code reviews and mentoring, lead initiatives to improve application reliability and observability, and leverage enterprise-approved AI-assisted engineering tools to accelerate delivery and enhance productivity. A key aspect of this role is evaluating, designing, and implementing AI-powered and agentic solutions to optimize software development, operational workflows, and engineering processes.

Plano, TX, United States onsite Full-time
PythonTypeScriptJava +5 more

Senior/Staff Applied Scientist

11d ago
G

Glean

Glean is building a world-class Data Organization composed of product data science, applied science, data engineering, and business intelligence groups. This is an applied science role based in our San Francisco office. You will collaborate with product data science and engineering teams to identify techniques, tooling, and process improvements in online A/B experimentation to assist rigorous decision-making across all relevant product domains. You will develop and maintain our A/B experimentation platform based on stakeholder feedback, and write code or identify vendors to deploy these techniques to production in a scalable manner that’s easy to use by engineering, product data science, product management, and design teams. You will also conduct end-to-end evaluation of various hero use cases like document/URL uploads, including evaluation set generation, coming up with evaluation criteria and methods to interpret the results. You will break down end-to-end evaluations into more granular evaluation of various tasks & skills including but not limited to content summarization/analysis/generation, multi-step reasoning & strategizing, tool selection & use, coding & system design. You will design, develop, and own best practices, tools, and processes across various evaluation problems, e.g., query intent classification, standardizing the use of best statistical principles to handle LLM stochasticity, and industry benchmarking.

$180k - $330k

Mountain View, CA; San Francisco, CA onsite
PythonLLMAI Agents +2 more

FAQs

Good questions, answered.

What to know about the AI and engineering roles listed here.

What counts as an AI Engineering job?

AiRagJobs 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 AiRagJobs 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.