Design Manager Jobs

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Staff Frontier Agent Engineer (Applied AI) , you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in. What You'll Build Frontier AI Systems Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use. Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, ent

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Senior Frontier Agent Engineer (Applied AI) , you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in. What You'll Build Frontier AI Systems Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use. Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterpri

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Frontier Agent Engineer (Applied AI) , you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in. What You'll Build Frontier AI Systems Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use. Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterprise data

Amplitude's Cloud Platform team builds the systems that every Amplitude engineer relies on every day to ship code — and we're rebuilding them for the AI era. As a Staff Platform Engineer, you'll set technical direction for the platform across teams, lead our highest-complexity and highest-leverage initiatives, and shape a platform where AI agents are first-class users alongside humans: kicking off deploys, opening pull requests against infrastructure, and triaging incidents, so a single engineer can get the throughput of a team. You'll operate across team boundaries — partnering with product engineering, fellow Staff+ engineers, and engineering leadership to make Kubernetes and cloud infrastructure effortless across the entire engineering org. You'll build the self-service automation, shared standards, and scalable AWS and GCP infrastructure that let dozens of product teams ship faster, safer, and with less cognitive load — and you'll multiply the engineers around you while you do it. Key Responsibilities Set technical direction — shape platform and domain-level technical strategy that improves developer experience, reliability, security, and cost, and lead the high-complexity, cross-cutting initiatives that deliver it with measurable impact for the organization. Drive clarity through ambiguity. Take on the most loosely-defined problems, validate the critical assumptions early, and create alignment with stakeholders across teams so others can move quickly and confidently — driving cross-team decisions to a timely close and escalating when needed. Build the AI-augmented platform. Design org-wide tooling, guardrails, and policy-as-code that help every engineer get more out of AI-assisted development — infra primitives an LLM can safely reason about and PR against, automated review, and standards that hold as AI changes how code gets written. Own Infrastructure-as-Code standards for Kubernetes, AWS, and GCP using Terraform, Helm, Kustomize, and emerging tooling — setti

GTM Operations & Strategy at MongoDB is a global team of builders and innovators focused on unleashing MongoDB’s sales greatness by pairing world‑class analytics with scalable operations. Within GTM Operations, the GTM Intelligence – Applied Science team turns complex GTM data into tools, models, and insights that help our sales organization make better, faster decisions across our people, segmentation, territory design, forecasting, and account prioritization. As a Senior Analyst on the Applied Science team, you will own high‑impact analytical workstreams end‑to‑end: from problem framing with senior GTM stakeholders, to data engineering and model design, through to productionalized workflows, dashboards, and executive‑ready narratives that drive concrete changes in the field. This role is based in Dublin, Ireland and supports a global stakeholder set across regions and GTM functions. We are looking to speak to candidates who are based in Dublin or Cork for our hybrid working model. What You’ll Do Translate GTM questions into analytical projects Partner with GTM Ops, Sales Strategy & Planning, Sales Leadership, and Central Analytics to scope problems, define success criteria, and prioritize work across areas like segmentation, territory design, account prioritization, and pipeline/forecast health. Structure ambiguous questions into hypotheses, analytical plans, and clear recommendations for senior stakeholders (SVPs, RVPs, functional leaders). Design and build scalable analytics & models Develop and maintain statistical and machine learning models (e.g., NARR prediction, deal qualification, account momentum, workload identification) that inform forecast expectations, territory assignments, and deal prioritization. Engineer robust data pipelines and features (SQL/Python) on top of our GTM data stack (Salesforce, product usage, call transcripts, marketing signals, etc.) in partnership with data and platform teams. Own core GTM analytics assets Contribute t

To design, develop, and maintain scalable full-stack applications for Industrial IoT and enterprise digital solutions, leveraging Angular, Node.js/Python, cloud technologies, and microservices to deliver secure, high-performance, and reliable software platforms. Source: Adani Group | Job ID: 54329

Senior Platform Engineer AI Engineering | Forward Deployed Engineering Role Description Defense Unicorns is seeking a senior AI Agentic Engineer to embed with a government team and its technology partners building an emerging AI-powered engineering ecosystem. This role will focus on designing, integrating, and operationalizing AI agents and agentic workflows within a secure enterprise environment. The engineer will work across large language models, agent frameworks, enterprise data, APIs, developer tooling, and platform services to turn AI capabilities into reliable, mission-relevant workflows. The ideal candidate is a hands-on engineer who understands both the capabilities and limitations of modern AI systems and can rapidly translate emerging technologies into working solutions. They should be comfortable experimenting with new models and agent frameworks, integrating agents with enterprise systems and tools, evaluating performance and reliability, and working alongside platform engineers and commercial technology partners. This is not a research-only role. The focus is on moving from AI concepts and prototypes to secure, useful, repeatable agentic capabilities that can operate in production environments. Responsibilities Design, build, and integrate AI agents and multi-agent workflows using modern LLM and agentic technologies. Develop agent capabilities that interact with enterprise applications, APIs, data sources, developer tools, and mission systems. Integrate models, agents, tools, and enterprise data into secure workflows that can be deployed in controlled and classified environments. Work with LLMs, RAG, embeddings, vector databases, tool/function calling, structured outputs, MCP, and related technologies. Develop mechanisms for agents to securely discover, access, reason over, and act upon approved enterprise data and services. Prototype and rapidly evaluate emerging models, agent frameworks, and AI capabilities to determine where they can create measurab

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

We design Spotify's consumer experience — end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints — from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. Platform & Partner Experience (PPX) sits within the Experience Mission, shaping how Spotify shows up across devices, platforms, and partner ecosystems. We build the foundations that power seamless integrations — from smart speakers to cars to AI agents — while ensuring every interaction feels personal, reliable, and unmistakably Spotify. As a Senior Staff Engineer, you'll advance how our systems power partner experiences at scale, tackling complex challenges that span teams, systems and organizational boundaries. You will work closely with platform, infrastructure, and product teams across the company to create shared foundations, streamline our architecture and unlock new experiences for our users and partners. What You Will Do Lead the technical strategy, architecture, and development of systems that enable and enhance partner and platform experiences across devices and integrations Drive complex technical initiatives that span multiple studios or a full mission — particularly where coordination cost, architectural sprawl, or competing local optimizations are part of the problem Create leverage by unifying fragmented approaches, retiring duplicative systems, and establishing the standards and platforms that enable multiple studios move faster without reinventing foundations — judging where consolidation creates leverage and whe

We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. The Policy & Safety team sits within Content Platform in the Experience Mission, building the systems that keep Spotify safe, compliant, and trusted by millions of users and creators. This team owns Spotify’s content moderation infrastructure — from detection models to policy enforcement systems and compliance data pipelines. Working at the intersection of machine learning, platform engineering, and regulatory compliance, the team partners closely with Trust & Safety, Legal, and Public Affairs. They’re on the critical path for every new content type and social feature — including messaging, comments, and collaborative experiences — ensuring safety is built in from day one. With a strong focus on “safety by default,” the team is investing in large-scale rearchitecture and ML-driven systems to proactively protect users and empower safer interactions across the platform. What You'll Do Design, build, and ship production-grade machine learning systems that power content safety and policy enforcement at Spotify scale Own and lead key technical initiatives across detection, classification, and policy evaluation systems Develop and maintain ML models for content moderation, including multimodal and LLM-based systems Build robust evaluation frameworks, including standardized datasets, offline and onlin

NVIDIA Silicon Co-Design Group (SCG) is a wide-ranging, multi-functional team at NVIDIA. We sit at the crossroads of design, architecture, marketing, operations, and productization. Our contributions span from the architecture stage through final product definition. We architect solutions for Datacenter, Server, Gaming, Robotics, Automotive, and Embedded markets. We are fast-paced, collaborative, and focused on making groundbreaking, impactful products. We are seeking a Senior Silicon Validation Methodology Engineer for Low-Power (LPWR). You will own the department LPWR validation methodology across GPU, SoC, and CPU, from architecture to silicon to product. What you'll be doing: Own architecture, design, and first-use of end-to-end LPWR validation methodology from silicon to product, covering corners, edge conditions, variability, and real-world scenarios. Set the department standard for LPWR validation so it is reused across a broad array of silicon products. Define what needs to be tested, why, and how — including coverage bounds, PVT/variability, test-time vs. schedule, signoff criteria, milestone playbooks, and SOL(speed of light) expectations. Invent and land new LPWR validation approaches that go beyond extending an existing test plan. Challenge current paradigms on performance, power, idle vs. active behavior, and corner-case coverage, and drive the resulting methods, tools, and metrics into org default practice. Set LPWR validation strategy and policy across teams, including long-lead items that outlive a single execution cycle. Translate architecture and product goals into multi-generation methodology, infrastructure, and tool investments. Drive cross-functional consensus with Architecture, Design, Firmware, and Software under conflicting PPA, coverage, latency, and schedule priorities. Improve validation workflow at department scale by building and adopting automation, coverage m

NVIDIA’s Silicon Co-Design Group sits at the crossroads of architecture, silicon, systems, and manufacturing, where first-principles thinking and engineering judgment at the highest level translate directly into product outcomes at scale. Our team gets every GPU, SoC, and CPU silicon program from first power-on to high-volume production! We are hiring a Senior Silicon Product Engineer – Manufacturing to join our Testability, Manufacturability, Reliability & Quality (TMRQ) team. In this role, you will co-design and implement SLT Production diagnostics for silicon features in a high-volume manufacturing environment, verify POR specifications, and support post-silicon validation for some of the industry’s most complex semiconductor chips. You should have experience defining and validating SLT production coverage specifications on silicon and translating product requirements into effective test coverage that delivers quality, yield, and efficiency at scale. Hands-on lab experience with system-level post-silicon bring-up and silicon debugging is required. This role sits at the intersection of multi-functional HW/SW teams and Production Operations to make manufacturing an outstanding part of the overall Silicon co-design cycle, production release, and HVM testing! The exceptional hire continuously improves high-volume manufacturing workflows and products multi-functionally and uses AI deliberately to drive impact while managing risk. What you will be doing: Partner multi-functionally with HW engineering teams such as Silicon PVT design and characterization teams, IO design and validation teams, system feature owners, SLT and BLT Production Operations, etc., to determine test coverage and constraints to resolve appropriate SLT test conditions, test content, and flows for high-volume manufacturing screens. Partner with SW and platform teams to support and improve SLT testing infrastructure, debug knobs, and data col

To support the landscape design team in developing creative and functional landscape concepts. To assist in preparing detailed drawings, visualizations, and design documentation. To contribute to site coordination and vendor interactions during project execution. To ensure landscape designs align with project requirements and quality standards. To build professional expertise in landscape planning through hands-on project involvement. Source: Adani Group | Job ID: 60224

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . We’re looking for a Senior Staff Machine Learning Engineer to lead Reddit’s next-generation user understanding initiative: building a unified, high-fidelity representation of each user that powers personalization across the platform. This role requires deep expertise in mainstream ML user modeling approaches (e.g., large-scale embeddings, user interest modeling, affinities, behavioral signals) and the ability to reimagine these systems in the GenAI era—leveraging LLMs and foundation models to unlock step-change improvements in fidelity, adaptability, and expressiveness. You will set the technical direction for this space, leading the design and implementation of Reddit’s core user representation layer—spanning embeddings, interest modeling, and key user attributes. You’ll ensure this foundation is scalable, reliable, and widely adopted across Feeds, Search, Notifications, and Ads, partnering closely with product, infrastructure, and downstream ML teams to drive measurable impact. This is a high-impact role. The systems you build will shape how hundreds of millions of people experience Reddit every day—what they see, what they discover, and the communities they connect with. Your work will directly advance personalization and relevance at global scale, strengthening Reddit as a platform for meaningful connection and belonging. What you'll do: Design User Understanding Strategy: Define a unified user understanding framework and strategy: how users are represented (embeddings, tags, attributes, LLM-based user profile), how