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DataHub is an AI & Data Context Platform adopted by over 3,000 enterprises, including Apple, CVS Health, Netflix, and Visa. Innovated jointly with a thriving open-source community of 13,000+ members, DataHub's metadata graph provides in-depth context of AI and data assets with best-in-class scalability and extensibility. The company's enterprise SaaS offering, DataHub Cloud, delivers a fully managed solution with AI-powered discovery, observability, and governance capabilities. Organizations rely on DataHub solutions to accelerate time-to-value from their data investments, ensure AI system reliability, and implement unified governance, enabling AI & data to work together and bring order to data chaos. About DataHub DataHub transforms enterprise data into trusted context, enabling intelligent decision-making by humans and AI agents. The company was founded by the creators of the popular DataHub open source product that has more than 16,000 contributors and is used by thousands of organizations. The company’s flagship product, DataHub Cloud, is the leading context management platform trusted by the Global 2000 to ensure that context is always relevant, reliable, and continuously refreshed across the entire data estate. The Role DataHub | Reports to Chief Marketing Officer Partnerships are a core lever in DataHub's growth. We're hiring a Director of Alliances to own our partnership strategy end to end: building the systems that let partners help us close deals faster, deliver complete solutions, and generate new pipeline. You'll build and run three interlocking motions: co-selling with hyperscalers and technology partners, certifying systems integrators to implement DataHub, and co-marketing with partners to source net new opportunities. This is a builder role. You'll design the playbooks, negotiate the agreements, and run the day to day partner relationships that turn strategy into revenue. What You'll Own Partner-Influenced (Co-Sell) Build and run a structure

The goal of a Senior Machine Learning Engineer at Scale is to own how we apply generative AI, agentic AI, computer vision, and reinforcement learning to mission-critical problems in production. Our senior machine learning engineers are handed problems that don't yet have an established approach, they propose the architecture, build it with support from other engineers, and are accountable for whether it holds up in the environments our customers depend on. Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge . Our work spans multiple modalities, with our primary focus on agentic systems built on large language models. We are developing agents that solve complex operational and planning challenges for government partners: agent frameworks that integrate custom retrieval pipelines and production APIs, memory and context-management systems that hold state across long-running tasks, geospatial reasoning over maps and spatial data, and the evaluation tooling that benchmarks and refines agent behavior. We also apply reinforcement learning in targeted places where it earns its keep, and our computer vision work advances evaluation, labeling efficiency, and multimodal model training in support of defense applications. As a Senior MLE, you'll have design authority over a capability area - the final say on the patterns used within your team, and the responsibility to make those patterns work under real constraints: classified environments, limited compute, and correctness requirements that don't bend. You will: Own the design and delivery of agent capabilities end to end - architecture, implementation, and the evaluation that proves they work Define net-new patterns in problem spaces with no established approach, propose them to the wider team, and lead the work to build them Take state of the art models developed internally and from the commu

As a Senior Software Engineer at Coder, you’ll build and evolve the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on, solve complex technical problems, and work closely with Product, Design, and other engineers to ship reliable agentic experiences. What you’ll do here Design and build production systems in Go, with work across React and TypeScript where needed. Improve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Build reliable integrations between agents, workspaces, tools, and developer infrastructure. Own projects from implementation through rollout and iteration. Contribute to design reviews, code reviews, and technical discussions. Partner with Product and Design to turn agent capabilities into useful developer experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Strong experience building and operating production software systems. Hands-on experience with Go. Experience with React and TypeScript. Experience building systems around LLMs or agentic workflows. Familiarity with model APIs, tool calling, context management, or agent loops. Good understanding of distributed systems and production reliability. Working knowledge of AWS. Strong problem-solving skills and comfort working through technical ambiguity. Someone who contributes beyond their own code through reviews, collaboration, and knowledge sharing. Bonus tacos if you have Experience building coding agents, developer tools, or cloud development environments. Experience with MCP, agent tools, or multi-agent systems. Experience with remote execution, sandboxing, or isolated compute. Experience building integrations across multiple model providers. Experience with AWS, Kubernetes, Docker, or L

As a Senior Software Engineer on Coder’s Agentic Engineering team, you’ll build and evolve the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on, solve complex technical problems, and work closely with Product, Design, and other engineers to ship reliable agentic experiences. What you’ll do here Design and build production systems in Go, with work across React and TypeScript where needed. Improve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Build reliable integrations between agents, workspaces, tools, and developer infrastructure. Own projects from implementation through rollout and iteration. Contribute to design reviews, code reviews, and technical discussions. Partner with Product and Design to turn agent capabilities into useful developer experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Strong experience building and operating production software systems. Hands-on experience with Go. Experience with React and TypeScript. Experience building systems around LLMs or agentic workflows. Familiarity with model APIs, tool calling, context management, or agent loops. Good understanding of distributed systems and production reliability. Working knowledge of AWS. Strong problem-solving skills and comfort working through technical ambiguity. Someone who contributes beyond their own code through reviews, collaboration, and knowledge sharing. Bonus tacos if you have Experience building coding agents, developer tools, or cloud development environments. Experience with MCP, agent tools, or multi-agent systems. Experience with remote execution, sandboxing, or isolated compute. Experience building integrations across multiple model providers. Experience with AW

As a Senior Software Engineer on Coder’s Agentic Engineering team, you’ll build and evolve the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on, solve complex technical problems, and work closely with Product, Design, and other engineers to ship reliable agentic experiences. To provide substantive overlap with the team, this position must be in Eastern Time. What you’ll do here Design and build production systems in Go, with work across React and TypeScript where needed. Improve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Build reliable integrations between agents, workspaces, tools, and developer infrastructure. Own projects from implementation through rollout and iteration. Contribute to design reviews, code reviews, and technical discussions. Partner with Product and Design to turn agent capabilities into useful developer experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Strong experience building and operating production software systems. Hands-on experience with Go. Experience with React and TypeScript. Experience building systems around LLMs or agentic workflows. Familiarity with model APIs, tool calling, context management, or agent loops. Good understanding of distributed systems and production reliability. Working knowledge of AWS. Strong problem-solving skills and comfort working through technical ambiguity. Someone who contributes beyond their own code through reviews, collaboration, and knowledge sharing. Bonus tacos if you have Experience building coding agents, developer tools, or cloud development environments. Experience with MCP, agent tools, or multi-agent systems. Experience with remote execution, sandboxing, or isolated compute.

As a Senior Software Engineer on Coder’s Engineering team, you’ll build and evolve the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on, solve complex technical problems, and work closely with Product, Design, and other engineers to ship reliable agentic experiences. What you’ll do here Design and build production systems in Go, with work across React and TypeScript where needed. Improve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Build reliable integrations between agents, workspaces, tools, and developer infrastructure. Own projects from implementation through rollout and iteration. Contribute to design reviews, code reviews, and technical discussions. Partner with Product and Design to turn agent capabilities into useful developer experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Strong experience building and operating production software systems. Hands-on experience with Go. Experience with React and TypeScript. Experience building systems around LLMs or agentic workflows. Familiarity with model APIs, tool calling, context management, or agent loops. Good understanding of distributed systems and production reliability. Working knowledge of AWS. Strong problem-solving skills and comfort working through technical ambiguity. Someone who contributes beyond their own code through reviews, collaboration, and knowledge sharing. Bonus tacos if you have Experience building coding agents, developer tools, or cloud development environments. Experience with MCP, agent tools, or multi-agent systems. Experience with remote execution, sandboxing, or isolated compute. Experience building integrations across multiple model providers. Experience with AWS, Kuber

As a Staff Software Engineer on Coder’s Agentic Engineering team, you’ll shape the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on while setting the team's technical direction. You’ll lead complex work, make sound architectural decisions, and help other engineers do their best work. What you’ll do here Set technical direction across Coder’s agent harness, integrations, and workflows. Design and build production systems in Go, with work across React and TypeScript where needed. Evolve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Lead complex projects from early ambiguity through production. Raise the engineering bar through design reviews, code reviews, and technical mentorship. Partner with Product and Design on clear, useful agent experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Deep experience building and operating production software systems. Strong hands-on experience with Go. Experience with React and TypeScript. Hands-on experience building systems around LLMs and agentic workflows. Experience with model APIs, tool calling, context management, or agent loops. Strong distributed systems knowledge. Working knowledge of AWS. A track record of setting technical direction without formal authority. Strong architectural judgment and comfort working through ambiguity. Someone who makes the engineers around them better. Our tech stack Backend: Go, Postgres Frontend: TypeScript, React Infrastructure: AWS, Kubernetes Observability: Prometheus, Grafana CI/CD: GitHub Actions Bonus tacos if you have (Tacos? If you need an ice-breaker, ask how we say thanks by giving tacos!) Experience building coding agents, developer tools, or cloud development environm

Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. About the Opportunity You will be responsible for the core architecture design and technical delivery of an enterprise-grade AI Native engineering platform, driving the deep integration of large language models, AI Agents, knowledge capabilities, and the software development lifecycle. The platform will support intelligent capabilities across requirement analysis, solution generation, coding collaboration, test automation, and environment orchestration.This role focuses on engineering-grade AI implementation in complex R&D scenarios. You are expected to turn model capabilities into reusable, scalable, and governable platform capabilities, and continuously drive the evolution of the engineering platform toward an AI Native architecture. What You’ll Be Doing Lead the overall architecture design and core capability development of the AI Agent platform, including task orchestration, tool invocation, context management, memory mechanisms, and multi-agent collaboration. Drive the implementation of AI Native capabilities in engineering workflows , including AI coding, technical solution generation, test case generation, test report generation, and automated environment deployment. Build the knowledge augmentation layer for the platform, including the d

OKX will be prioritising applicants who have a current right to work in Singapore, and do not require OKX's sponsorship of a visa. Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity You will be responsible for the core architecture design and technical delivery of an enterprise-grade AI Native engineering platform, driving the deep integration of large language models, AI Agents, knowledge capabilities, and the software development lifecycle. The platform will support intelligent capabilities across requirement analysis, solution generation, coding collaboration, test automation, and environment orchestration.This role focuses on engineering-grade AI implementation in complex R&D scenarios. You are expected to turn model capabilities into reusable, scalable, and governable platform capabilities, and continuously drive the evolution of the engineering platform toward an AI Native architecture. What You’ll Be Doing Lead the overall architecture design and core capability development of the AI Agent platform, including task orchestration, tool invocation, context management, memory mechanisms, and multi-agent colla

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Staff+ Software Engineer, Developer Acceleration About the Role Anthropic is seeking talented and experienced Staff+ Software Engineers to join Developer Acceleration within the Developer Productivity organization. The Developer Acceleration team is responsible for the infrastructure that enables thousands of employees to be productive via agents. By joining this team, you'll have a direct impact on the velocity and quality of Anthropic's research and engineering development. You will be defining how AI transforms our work and building the future of agentic productivity. Some things that the team owns: Agent Runtime Platform for provisioning and running agents Knowledge for providing relevant internal context to agents and humans Some of our work is mentioned in When AI Builds itself and Building effective human-agent teams . Responsibilities Define the future of agentic productivity at scale and at the frontier Own the technical strategy and roadmap for your area, translating goals into concrete execution Define what quality means for agent-driven engineering work, and hold that line across the company Stay hands-on: build and ship the runtime platform and tooling Build agent harnesses and experiment with various context management strategies to improve agent performance and correctness Write evals to benchmark agent behaviors Deliver impact by collaborating cross-team Own infrastructure scalability and reliability, and establish operational excellence practices You May Be a Good Fit If You: Have 10+ years building and operating large-scale distributed systems Have 3+ years of experience leading large scale, complex projects or

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. Staff Software Engineer (Full Stack - Backend) Staff Software Engineer The Product Auth0 is a developer-friendly identity platform designed by developers, for developers, simplifying authentication and authorization for more than 100 million daily logins worldwide. Within this global platform, our team drives two of Auth0’s most critical, high-impact surfaces: The Manage Dashboard: The definitive front door for developers and enterprise administrators worldwide. It is the control center where our customers configure critical identity policies, manage complex organizations, and monitor active threat intelligence. The Auth0 Marketplace: Our rapidly expanding extensibility ecosystem. The Marketplace allows customers to seamlessly discover and install third-party integrations - such as security monitoring, consent management, and database connectors - to dynamically customize their identity pipeline. The Role As a Staff Engineer on this team, you will not just write code; you will own the technical strategy, architect highly resilient frameworks, design developer-friendly APIs, and mentor a high-performing team of engineers. Your architectural ownership will span the entire stack - from the React-based frontend down through our Node.js/Go services, robust operational pipelines, and PostgreSQL databases. What You Will Do: Product Ownership: Lead the technical lifecycle of your team’s products, ensuring they are robust, secure, and performant from the frontend to

Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. Our work spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective. We control the full stack: the models, the agent harnesses, and the search infrastructure underneath. That gives us the freedom to develop new approaches across all three training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work. Responsibilities Push search and agent quality forward through improvements to models, training data, tools, and system design. Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion. Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively. Design and build agent harnesses around the tools, context management, execution environments, and orchestration that support reliable work over many steps. Improve retrieval and ranking models and the search interfaces agents use to find and assess information. Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements. Own experiments end to end, from a clear hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost. Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production. Qualifications A strong track record of building and shipping ML systems, with deep experience in one or more of LLM post-training, reinforcement learning, search and retrieval, or agent systems. Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems. Experience designing rigorous evaluations

Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. Our work spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective. We control the full stack: the models, the agent harnesses, and the search infrastructure underneath. That gives us the freedom to develop new approaches across all three training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work. Responsibilities Push search and agent quality forward through improvements to models, training data, tools, and system design. Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion. Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively. Design and build agent harnesses around the tools, context management, execution environments, and orchestration that support reliable work over many steps. Improve retrieval and ranking models and the search interfaces agents use to find and assess information. Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements. Own experiments end to end, from a clear hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost. Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production. Qualifications A strong track record of building and shipping ML systems, with deep experience in one or more of LLM post-training, reinforcement learning, search and retrieval, or agent systems. Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems. Experience designing rigorous evaluations

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Job Summary We are seeking a motivated engineer to join the DRAM Systems Engineering team, focusing on the development, evaluation, and optimization of next-generation memory systems for AI accelerators. This role emphasizes research and development across hardware architecture, operating systems, and performance analysis to support Agentic AI inference workloads. If you are ambitious and eager to make an impact in the exciting world of AI and memory systems, this is the perfect opportunity for you! Responsibilities Characterize AI inference workloads and examine memory behavior Build and evaluate tiered memory hierarchies for AI accelerators Study KV cache lifecycles, MoE models, and data placement strategies Compare and optimize explicit versus hardware-assisted data movement Develop, test, debug, and detail system-level and OS components Prototype and evaluate agentic AI systems by building agents and multi-agent workflows using modern frameworks and orchestration patterns (planning, tool use, memory, and context management). Apply these technologies both as workloads under study and as accelerators for internal engineering workflows <h2 style="color:!importan

AI Software Engineer, Agent Harness Location: Bengaluru, Karnataka (or throughout India remote-friendly with travel) About EnCharge AI EnCharge AI is building the next generation AI platform. Our novel in-memory-computing architecture delivers a 10x step-function improvement in compute energy efficiency and performance for AI inference workloads. As the demands of artificial intelligence move beyond today's models, we believe fundamental underlying infrastructure must evolve. We are an experienced team of AI researchers, silicon & systems engineers, and architects backed by leading investors, poised to become the essential platform for the next wave of AI innovation. The Opportunity We serve open-weight models and our own bespoke checkpoints on EnCharge hardware. The models change often, and the harness around them needs to keep up. You own this layer that runs agents against files, tools, documents with permissions, memory, unattended execution, and real outputs. It will be assembled from a combination of open-source and bespoke code. Key Responsibilities Own the harness architecture end to end — agent loop, safe execution, context management, knowledge base, memory, permissions, orchestration, outputs, interfaces, observability — one component per layer, with clear interfaces so layers can be swapped. Build the pieces with no open-source equivalent e.g. session semantics, enforced permissions, memory in a human-editable file, orchestrator, and outputs. Keep pace with the models: adapters, prompt formats, tool-call schemas, stop conditions, benchmarking and evaluation. Make tool use reliable across models of uneven tool-calling quality — validation, repair, retries, fallbacks. Develop agents, tools, and MCP servers for internal and customer use cases, and review them for security before they ship. Build the evaluation harness: task suites, regression runs on every model or harness change, cost and latency per task alongside quality. Define the interfaces: