Ai Agent Engineer Jobs

Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. Our research spans: Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science Imaging foundation models trained across the world's largest microscopy datasets Reasoning and agentic systems that connect frontier LLMs with biological foundation models Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled Join Our Team! As a Research Engineer, you'll build the models and systems that define what AI can do in biology: foundation models, reasoning, reinforcement learning, and multi-agent systems at frontier scale. What You'll Do Build on and advance the AI systems at th

What we're building Mutiny is the self-improving AI infrastructure for GTM teams to execute faster and close more revenue. Our ambition is to do for revenue velocity what Cursor and Claude Code did for engineering velocity. With Mutiny, everyone in sales and marketing gets a bench of GTM athletes that handle any work across their revenue motion and learn from what's actually moved their deals. In April we re-launched the product as an agent-first platform. Anthropic showcased us as a leader in AI GTM. MRR is growing more than 70% month-over-month, with customers like Uber, Rippling, and Snowflake. We're backed by Sequoia, YC, and Insight, and we're building a generational company. The opportunity Most engineers spend their career making predictable systems faster. You'll spend yours making non-deterministic ones trustworthy. As a senior engineer on our AI product team, you'll architect the Campaign Builder and Agent experiences marketers and sellers open every day to go from idea to personalized assets in minutes. You'll partner directly with product, design, and the founders to define what an agent-first GTM platform should feel like, and your calls on architecture, evals, and guardrails compound across thousands of customer accounts. This role is in person in New York City, five days a week, and we ship weekly. What you'll own The core agent surfaces. Architect and ship the Campaign Builder and Agent experiences end-to-end. Frontend, backend, prompts, evals, the whole stack. Reliability on top of LLMs. Make non-deterministic models feel deterministic at the surface. Build the retries, fallbacks, and orchestration so the customer never sees the failure mode. Evals and guardrails. Define how we measure quality, catch regressions, and keep brand and tone consistent across thousands of customer accounts. Speed and feel. AI products live or die by latency and the loop between intent and output. You'll obsess over both, and use coding agents and agent networks to ship f

ABOUT THE TEAM The AI Foundations Team at Mural is pioneering how generative AI transforms visual collaboration and decision-making. We’re a remote-first group of engineers, designers, and product thinkers focused on helping teams work together more effectively. Our goal isn’t to replace human creativity. It’s to amplify it, building AI that enhances how people align, communicate, and make decisions visually. YOUR MISSION You will design and build the core AI systems and platforms that enable Mural’s next wave of agentic, AI-driven collaboration experiences. Rather than building isolated AI features, you’ll work on the core backend systems that power Mural’s agent platform, including agent orchestration, durable execution, contextual memory, tool integration, observability, and evaluation. Your work will enable intelligent agents to reason over product context, act on behalf of users, and operate reliably and safely at scale. Our stack at Mural includes Azure OpenAI, React, Node, MongoDB. WHAT YOU'LL DO Build the core backend systems that power Mural’s agent platform, including orchestration, durable execution, tool execution, memory, observability, and evaluation infrastructure Design scalable services and APIs that allow AI agents to retrieve context, coordinate multi-step workflows, interact with Mural data, and act reliably on behalf of users Develop the agent memory layer, including systems for conversation context, product context, retrieval, summarization, compaction, and long-term context management Create infrastructure to monitor, debug, and improve agent behavior through traces, metrics, feedback loops, and offline evaluation Translate complex, open-ended product needs into clear backend architectures, service boundaries, data models, and implementation plans that align technical capabilities with user value Help define the technical direction for agentic AI at Mural, contributing to long-term architecture and strategy Champion engineering excellence, men

About the Team The Recursive Self-Improvement (RSI) team works across research, engineering, product, and infrastructure to build AI systems that accelerate and ultimately conduct high-quality research at OpenAI. We work to automate real research workflows and improve research productivity by building systems and feedback loops, designing evaluations, and training models to develop missing capabilities. Our work spans the full lifecycle of model training, evaluation, and deployment to help researchers move faster and tackle increasingly ambitious problems. About the Role We’re hiring research scientists , research engineers , and AI systems engineers to work on automating research at OpenAI. This role is based in San Francisco, CA. In this role, you will: Design evaluations for research judgment, hypothesis generation and testing, and long-horizon experiment execution. Turn real research workflows and model failures into data and evaluation flywheels. Improve model research capabilities through agent harnesses, synthetic data, RL environments, and model training. Build and maintain safe, reliable integrations between our models and OpenAI’s research infrastructure. Develop research agents, experiment-orchestration systems, and sandboxed runtimes that support real research workflows. Create metrics and economic models to understand RSI’s current and future effects on research productivity, model capabilities, and the safety of internal deployments. This is a high-ownership role for researchers and engineers who thrive in ambiguity, move fluidly between research and implementation, and turn emerging opportunities into rigorous, reliable, scalable results. You might thrive in this role if you: Have research or engineering experience across LLM training, model evaluations, agent systems, synthetic data, research infrastructure, or large-scale distributed systems. Are a strong generalist who can move between open-ended research and practical implementation, turning ambig

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. How do you make the world's most powerful coding agent for data a delight to use? We are the fastest growing software company at this scale in history and looking to push that to new heights by building the best coding agent in the industry. The Cortex Code (CoCo) team is building the future of coding agents for working with data. See our flagship product in action: CoCo Desktop in Action . As a Frontend AI Principal Engineer, you are the cornerstone of delivering the ultimate user experience. You will be entrusted with the highest level of polish, creativity, and interaction design, pushing the boundaries of what's possible in the most innovative areas of our product. This role offers the unique opportunity to have broad creative license, empowering you to lead and create groundbreaking systems and experiences that marry aesthetics with functionality, making data more accessible and actionable for our customers. AS THE FRONTEND AI PRINCIPAL ENGINEER YOU WILL: Design and build exceptional experiences, ensuring the highest level of polish, creativity, and interaction to solve complex customer problems. Define the architectural vision for the CoCo coding agent platform and ensure consistency of design abstractions across the entire product surface Develop innovative platform

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level. ABOUT THE ROLE: You will work on critical business initiatives in the core database engine, bring an AI-forward approach to software development and accelerate roadmap cycles for the benefit of our customers. Your work will directly impact how developers and businesses build with data. You'll own the full AI engineering lifecycle: design, prompt/tool engineering, evals, deployment, measurement, and optimization. You'll work with a small, high-powered engineering team. What you will do in this role: Own features end-to-end for Snowflake Database Engineering products. Build agentic workflows, coding harnesses, evaluation pipelines. Build enterprise-grade context engineering: function calling, tool schemas, guardrails, agent teams, and verification/repair. Design evals and hillclimb : create golden sets, create rubrics and metrics, analyze errors, run experiments to hill climb on the metrics. Partner with product and infra: translate customer problems into products and experiments. Collaborate with infr

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. At Snowflake, we are building a high-impact team to help the world's most innovative companies unlock the power of AI. As a Senior Forward Deployed Engineer, Applied AI on our Cortex AI team, you will be a hands-on technical leader and trusted partner to our most strategic customers. You will own the end-to-end delivery of enterprise AI programs, leading a team of 2–4 engineers while staying deeply technical yourself. You will set the technical direction for your customer engagements, mentor your team, and serve as the senior technical voice at the intersection of product, engineering, and customer success. IN THIS ROLE AT SNOWFLAKE, YOU WILL: Lead Customer Programs : Own the full lifecycle of complex, multi-engineer AI engagements – from scoping and architecture through deployment, monitoring, and handoff. Be accountable for delivery quality and customer outcomes for the projects you lead. Own AI Quality : Define what "good" means for each engagement. Translate ambiguous customer goals into measurable quality metrics, evaluation frameworks, and golden datasets – then run systematic eval loops to hill-climb on agent quality, catch regressions before customers do, and continuously raise the bar on accuracy, faithfulness, and safety. Set the standard for how the team measures

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! In September 2026 we raised a $350 million Series E at a $3.5 billion valuation , and we are scaling our engineering and research teams to meet demand. The role Frontier AI data is expensive to make and hard to measure. Every task we deliver is tested against the strongest models, often through many long-running agent rollouts. Your job is to make that process faster, cheaper, and more rigorous with ML and AI You will be one of the early members of ML & Research Engineering at Snorkel. You will study how frontier-grade data is generated and evaluated, form hypotheses, validate them against real production data, and ship the winners at scale. You will shape the discipline's direction, its standards, and the team that grows around it. What you'll work on Efficient agentic evals. Cut the cost of long-horizon agent evaluation with adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating. AI model routing. Route every eval and judge call to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution. Fine-tuned small models. Fine-tune and serve open-weight models (LoRA and other

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. The Opportunity Adyen is building a top-tier AI engineering organization in Amsterdam, San Francisco and Madrid to drive our next chapter of innovation using AI globally across the entire company. This is a highly technical, hands-on role focused on the exploration and application of cutting-edge AI research within the financial technology sector. As a Senior AI Research Engineer, you will operate with a high degree of autonomy and responsibility , delivering strategic, high-impact outcomes that bridge the gap between advanced AI research and production-grade applications at a global scale, potentially impacting trillions of dollars in transactions annually. What You'll Do: Innovate and Deploy: Drive the execution of Adyen's AI strategy , focusing on the practical application of Generative AI (GenAI) and other AI methodologies in finance . This includes contributing to Adyen's efforts in key research areas such as AI agents for data analysis and operational workflows , human-in-the-loop for integrity risk , and development of foundation models . For instance, you might contribute to initiatives like the Data Agent Benchmark for Multi-step Reasoning (DABStep) , which evaluates AI agents on real-world data analysis tasks, including those from the financial sector. Build Production-grade Applications: Bridge the gap between cutti

Job Details: Job Description: Intel's Design Quality and Reliability organization is seeking an AI Platform Engineer to architect and build an enterprise-grade AI platform for mission-critical engineering work. This platform will enable Intel engineers to analyze complex design, qualification, and reliability data; automate engineering workflows; access organizational knowledge; and make faster, evidence-based decisions throughout the product lifecycle. The successful candidate will combine strong software engineering fundamentals with expertise in AI-native and agentic development. They will be highly proficient with Agentic AI coding assistants and able to use these tools responsibly to accelerate architecture, implementation, testing, debugging, and documentation. This role requires close collaboration with Design, Quality and Reliability, Product Engineering, Manufacturing, IT, Information Security, and other Intel stakeholders. Responsibilities 1. Architect and develop Intel's reusable AI platform for Design Quality and Reliability. 2. Build AI agents and workflows for engineering data analysis, qualification planning, risk assessment, knowledge retrieval, reporting, and process automation. 3. Apply Agentic AI coding assistants to accelerate software development while maintaining rigorous engineering review and validation. 4. Integrate AI capabilities with Intel engineering databases, quality-management systems, internal APIs, spreadsheets, documentation repositories, and workflow tools. 5. Develop production-grade backend services, APIs, data pipelines, model gateways, and agent-orchestration components. 6. Establish shared platform capabilities for identity, access control, tool authorization, memory, observability, evaluation, and auditability. 7. Implement human approval, deterministic validation, and rollback controls for consequential engineering actions. 8.

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-thr

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Baseten's engineers want to work in an AI-first way. What's missing isn't enthusiasm — it's the platform underneath it. Today everyone assembles their own agent config, context files, and MCP servers, so the good patterns stay trapped in individual setups instead of becoming defaults everyone inherits. You'll build that platform: the agent configurations tuned to our monorepo, the context and tooling layer that makes agents competent in our codebase, the evals that tell us which approaches actually work, and the rollout mechanics that get a new engineer productive with agents in week one. You are not here to mandate how engineers use AI — you're here to make the good path the easy path. Success looks like teams adopting what you build because it beats what they'd cobble together themselves, not because a policy requires it. Platform engineer, not AI evangelist. Ship infrastructure, measure it, kill what doesn't work, let adoption be the referee. The playbook for AI-first SDLC doesn't exist at any company yet. You'll write ours. WHAT YOU'LL BUILD Agent substrate — Repo-level context infrastructure that makes agents competent in our codebase ( CLAUDE.md/AGENTS.md conventions, architecture and domain context, and the tooling to keep it accurate as code moves). Internal MCP servers giving agents scoped access to CI, observability, incident tooling, deployment state, and docs. Shared skills, subagents, and hooks th

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. AI and intelligent systems are driving the fifth paradigm shift, following previous technological revolutions like mainframes, personal computers, the internet, and mobile devices. We believe, in the foreseeable future, AI will revolutionize the FinTech industry - from how consumers understand and manage their finances, to how developers build applications and how all companies operate. The fintech industry landscape will undergo a fundamental reshape. Plaid in the FinTech AI Ecosystem Plaid is uniquely positioned to become the financial data and insights backbone for AI applications and platforms in this evolving ecosystem. We believe consumers should be able to understand and manage their financial life through conversational AI interfaces using natural language. We believe consumers should have peace of mind with a trustworthy consent and authorization manager when agents shop for them. We believe identity verification and financial fraud prevention in AI-powered products should feel seamless and embedded for the end users. The list goes on. The most important AI companies, major fintechs, and customer agent platforms are actively trying to integrate Plaid into AI-powered products and solutions t

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. You independently lead the most complex AI deployments Smartsheet undertakes. You own the full engagement lifecycle from technical discovery through production deployment through solutions org handoff. You architect multi-agent solutions, design client-personalized MCP resource packs, and build the Deployment Kits that transform how 200+ solutions consultants and partners operate. You mentor junior FDEs, drive the intelligence loop, and present field findings at weekly Applied AI strategy sessions. You are building a function, not filling a role. What You Will Do Lead complex, multi-system AI deployments end-to-end scope, architect, build, validate, and manage the customer relationship throughout. Own the AI workshop program for your pod, customize modules per customer, lead technical sessions, translate outputs into production requirements, evolve content from field learning. Architect multi-agent solutions selecting the right coordination pattern for each customer’s workflow characteristics and compliance requirements. Design client-specific and industry-specific MCP resource packs that serve personalized intelligence from the server so every connected AI surface gets smarter for that customer automatically. Own Deployment Kit quality for your pod. If a kit is not documented well enough for a solutions consultant with no engineering background to follow, it isn’t done. Lead Solutions Enablement Sprints: transfer AI deployment patterns to solutions consultants and partners with training materials and certification crite

We're building AI Teammates: agents that work like actual users in Asana and integrated apps. They triage bugs, respond to requests, draft project briefs, conduct research, and handle complex knowledge work across your team's workflows. Unlike chatbots, Teammates are shared team resources that build memory and context across all executions. They get smarter as you and your colleagues work with them. Currently in beta with Fortune 500 customers, AI Teammates represents Asana's shift from tracking work to getting work done. We're looking for an Engineering Manager to lead the Agent Orchestration team — the team building the connections to other systems that allow Asana AI to serve the highest-value workloads. This means building the integration capabilities, agent skills, and vertical use cases that make AI Teammates extraordinarily useful across enterprise tools and workflows. You'll manage a senior team of six engineers (ICs up to L6) working at the intersection of systems integration and emerging AI capabilities to architect the foundation that lets AI agents operate seamlessly across customer environments while meeting enterprise requirements for reliability and compliance. This is a rare opportunity to be at the forefront of Agentic AI. You'll work directly with model partners, lead the core team shaping how enterprises collaborate with AI agents across their entire tool ecosystem, and drive the technical direction for one of the most impactful applied AI challenges in the industry. You'll also flex into org-level engineering leadership across the broader Asana AI group, contributing beyond your direct team. About Asana AI Asana AI is the company's number one priority. We're building the future of human/AI collaboration — going beyond chatbots to integrate AI into everyday workflows for some of the biggest companies on the planet. Asana's AI Teammates deliver a secure, multi-player, enterprise-grade agentic experience. They're transforming Asana from a place wher