Inference Engineering And Product Lead in United States

NVIDIA Research is seeking extraordinary networking innovators to join our NVResearch team. As a research intern on this team, you will contribute to the development of future high-performance networking and computing systems. We are seeking a balanced background of research excellence in building systems and a deep understanding and broad perspective across the fields of computer architecture and communication systems for distributed computation. NVIDIA has pioneered programmable GPUs and the CUDA language, and this visionary Research team will take those technologies to the next level with its creative ideas and new inventions. This position offers you the opportunity to have a real impact while working with some of the most creative and forward-thinking people in the world who are here at this dynamic, technology-focused company. What you'll be doing: Develop algorithms and design hardware and software, extending the state of the art in computing, networking, and other technology areas surrounding NVIDIA's business. Invent new techniques, technologies, methodologies, processes, and devices, to enable new products or types of products. Deliverable results include prototypes, patents, and publications. Contribute to research that informs NVIDIA's technology direction 5-10 years out. Work focuses on long-horizon problems rather than products currently shipping or in development, except as to how they can be extended and improved. Projects can include but are not limited to: optimizing communication stacks for AI training and inference, designing network protocols and congestion control, co-designing AI systems across software and hardware, developing circuits and microarchitecture for network controllers and switches, and architecting networks built on optical switching and silicon photonics. What we need to see: Pursuing a

NVIDIA is seeking outstanding Research Interns to join the Data-Driven AI for Robotics (DAIR) group. The focus is on learning embodied skills from large-scale human data. Our objective is to develop AI systems that capture, understand, and reproduce complex human motion and interaction skills across physical and digital embodiments, including humanoid robots and animated characters. Our research spans the full stack: reconstructing human motion and human-object interactions from video; generating diverse, controllable character behaviors; transferring motion across embodiments; and training physically grounded controllers for humanoid robots and interactive virtual characters. You will collaborate with a passionate and supportive research team that consistently produces influential work published at leading computer vision, machine learning, graphics, and robotics conferences. You will also have the opportunity to collaborate with world-class research and product teams across NVIDIA, following our strong “one-team” culture. What you'll be doing: Innovate and implement novel AI algorithms that transform large-scale human data into controllable motion and interaction skills across physical and digital embodiments. Develop robust, scalable training and inference pipelines for motion reconstruction, generation, retargeting, and character and robot control. Build methods that transfer human skills to humanoid robots, including whole-body loco-manipulation and dexterous manipulation. Maintain a close, collaborative relationship with your mentor(s). Publish your research findings at leading computer vision, machine learning, graphics, and robotics conferences. Partner with product teams to enable effective technology transfer of your work. Research Topics Include: Human motion and human-object interaction reconstruction, synthesis, and generatio

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. As our TT-Distributed Software Engineer, you will develop and optimize distributed software systems that power the most efficient and highest-performing AI and HPC clusters. In this role, you'll work on distributed programming across multiple nodes, utilizing systems programming, inter-node communication, and Tenstorrent’s scalable architectures to advance the state-of-the-art distributed inference and training infrastructure. This role is hybrid, based out of Santa Clara, CA; Austin, TX; or Toronto, ON. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are Strong C or C++ engineer with solid foundations in systems programming, operating systems, and distributed systems principles. Enthusiastic about distributed computing, including IPC, socket programming, and cluster resource coordination. Comfortable reasoning about scalability, fault tolerance, and performance across multi-node environments. Curious and first-principles thinker who challenges conventional approaches to distributed system design. Motivated to grow into a deep technical expert in large-scale distributed AI infrastructure. What We Need Architect, implement, and optim

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions. Responsibilities: Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersect

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. We are looking for a motivated Deep Learning engineer to bring advanced communication technologies into AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, etc. You will be working with the team that created communication libraries like NCCL, NVSHMEM & technology like GPUDirect -- for scaling Deep Learning and HPC applications. Your customers will have diverse multi-GPU demands, ranging from training on scales up to 100K GPUs to inference down at microsecond latency. Communication performance between the GPUs has a direct impact on AI applications. Your work in AI toolkits will make all of those easier for the community. This is an outstanding opportunity for someone with an AI background to advance the state of the art in this space. Are you ready to contribute to the development of innovative technologies and help realize NVIDIA's vision? What you will be doing: Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities. Collaborate hands-on with teams working on the latest AI models. Improve AI compilers to hide communications or perform automatic fusion. Conduct in-depth AI workload performance characterization on multi-GPU clusters. Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads. Author

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. NVIDIA has a rapidly expanding ecosystem of data center platform designs. From single node HGX/DGX systems all the way up to large multi-node NVLink domain rack architectures. These designs have become core to NVIDIA's rapidly growing enterprise and cloud provider businesses. Each brings together the full power of NVIDIA GPUs, NVIDIA NVLink, NVIDIA InfiniBand networking, NVIDIA Grace CPUs, and a fully optimized NVIDIA AI and HPC software stack. We are searching for a highly motivated engineer to lead performance benchmarking and optimization efforts for our data center products. You will be instrumental in ensuring our data center solutions deliver industry-leading performance for accelerated computing workloads. What you will be doing: Design and execute comprehensive performance benchmarking strategies for our data center platforms and products Characterize real-world AI training, inference, and HPC workloads at scale Define, track, and report key performance indicators (throughput, latency, efficiency, scaling) Build automation tools and frameworks for performance monitoring and analysis Identify and analyze performance bottlenecks across compute, memory, network and storage subsystems Work closely with architecture, hardware,

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. Plaid Protect is a real-time fraud intelligence product built on a unique advantage: Plaid’s network-level visibility across bank accounts, devices, identities, sessions, institutions, applications, and financial behavior. Protect helps customers detect first-party fraud, synthetic identities, account takeovers, and coordinated attacks that are difficult to see from a single application, account, or transaction. Trust Index turns that fraud intelligence into real-time fraud scores and actionable attributes. This team builds the systems that make this intelligence possible: low-latency inference, new data and model integrations, customer-facing APIs and attributes, safe rollouts, and feedback loops. Ti3 expanded Plaid’s fraud graph nearly 10x and, in early testing, detected up to 41% more fraud at the same false-positive rate. Learn more about Ti2 and Ti3 . We are a small, high-agency team working closely with Product, Data Science, and Machine Learning. We value demos over docs, conviction over consensus/alignment, builder schedule over meeting-heavy calendars. We’re scrappy and a talent-dense team that has high agency and high ownership. As a Staff Software Engineer on the Protect Core team, you wi

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? We're building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most curious people to help us do it. As a Lead Data Scientist on our Analytics and Data Insights team, you'll tackle problems that don't have textbook answers yet; shaping go-to-market strategy for technology that's still being invented, designing the experiments that prove or kill our biggest bets, and helping enterprises understand what foundational AI actually means for their bottom line. You'll own the full analytical lifecycle, from framing the right questions and building the models, to leading a team that delivers answers leadership can act on. As a Lead Data Scientist, you will: Drive the mission forward. Own the science: design and lead experimentation programs including A/B tests, multi-armed bandits, causal inference studies, that directly map to product and go-to-market decisions. Build predictive models that matter: develop and deploy models for forecasting, segmentation, propensity scoring, and opportunity sizing across Cohere's core business lines. Lead and grow a tea

About Pinecone Pinecone is the knowledge infrastructure for AI at scale. Its leading vector database and knowledge engine, Pinecone Nexus, power accurate, performant AI applications for more than 9,000 customers and 800,000 developers worldwide. Pinecone's mission is to make AI knowledgeable. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. About the Team and Role: We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities: Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation Design and build optimized indexing pipelines for structured and unstructured data Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration Improve retrieval quality through evaluation and observability frameworks Design APIs for internal and external user and agentic consumers Optimize latency, throughput and cost across large-scale inference and retrieval workloads Drive technical direction for reliability and security What You’ll Bring to the Table: To thrive in this role, you don't need to check every single box, but you should be deep

About Pinecone Pinecone is the knowledge infrastructure for AI at scale. Its leading vector database and knowledge engine, Pinecone Nexus, power accurate, performant AI applications for more than 9,000 customers and 800,000 developers worldwide. Pinecone's mission is to make AI knowledgeable. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. About the Team and Role: We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities: Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation Design and build optimized indexing pipelines for structured and unstructured data Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration Improve retrieval quality through evaluation and observability frameworks Design APIs for internal and external user and agentic consumers Optimize latency, throughput and cost across large-scale inference and retrieval workloads Drive technical direction for reliability and security What You’ll Bring to the Table: To thrive in this role, you don't need to check every single box, but you should be deep

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Airbnb, a leader in travel and hospitality, is on a mission to create a world where anyone can belong anywhere. Long-Term Stay & Luxe is being built with 0-to-1 intensity inside a marketplace that already moves at massive scale, and this team needs people who can operate like founders while still respecting the rigor of a mature, two-sided marketplace. The Difference You Will Make: Airbnb is seeking a Staff, Advanced Analytics to be the analytics owner and strategic partner for Long-Term Stay & Luxe — setting the agenda for what gets measured and tested, building the quantitative models that underpin how the business grows, and operating as a peer to product and business leadership. A Typical Day: Own the end-to-end analytics strategy and roadmap for Long-Term Stay & Luxe — decide what to measure, what to test, and where the biggest leverage is. Prioritization decisions should exist because of your recommendations. Build and evolve the quantitative models that run the business: supply-demand balance, marketplace liquidity, pricing and take-rate models, and LTV/forecasting — going beyond dashboards to build the underlying data models and pipelines. Design and analyze experiments across pricing, search and ranking, host onboarding, and guest conversion. Develop causal-inference approaches (holdouts, proxy metrics, quasi-experimental methods) for the many cases where a clean randomized trial isn't possible given lower volumes or longer booking cycles. Act as a proactive strategic partner to product, GM, and cross-functional leaders — surface insights and recomme

About the Team Our economics team is continuously working to improve our understanding of an AI-driven economy. About the Role We are seeking a highly technical Economist to join the OpenAI Economic Research team studying the real-world economic impacts of AI. This role is designed for economists with up to 5 years of professional experience post-Ph.D. who are interested in using novel, large-scale datasets to study how AI is reshaping economic systems. We are looking for candidates with deep expertise in at least one core domain relevant to AI’s economic impact, and an interest in contributing to a broader research agenda spanning labor markets, firm behavior, market dynamics, and macroeconomic change. This is an individual contributor role where the candidate will organize and execute on their own data-oriented projects. You will work at the intersection of economic research, data science, and public policy to produce rigorous empirical work that informs decision-makers across the public, industry, and government. Research Areas of Interest We are particularly interested in candidates with demonstrated expertise in one or more of the following areas: Economic Measurement of AI Impact (e.g., adoption trajectories, labor market transitions, productivity growth, and forecasting/scenario modeling for AI-driven economic change) Macroeconomic Implications of AI (e.g., productivity, technology diffusion, economic growth) AI and the Labor Market (e.g., employment, wages, job search, task-level impacts, skill acquisition) Applicants are not expected to have experience across all domains. We aim to build a team with complementary strengths across these areas. In this role, you will: Design and execute empirical research using large-scale observational or experimental data. Apply causal inference and/or structural modeling techniques to study AI-driven economic change. Collaborate with cross-functional teams to translate research questions into testable frameworks and applic

About the Team The Foundations Research team works on high-risk, high-reward ideas that could shape the next decade of AI. Our goal is to advance the science and data that enable our training and scaling efforts, with a particular focus on future frontier models. Pushing the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science. The Search team sits within Foundations, building agentic search by co-designing model–system interfaces with the core search stack (serving, indexing, retrieval) to translate model intent into reliable, real-world actions. Operating at the frontier of AI and information retrieval, the team develops large-scale systems that transform and index vast corpora, enabling models to reason over global knowledge and act dependably. In close partnership with researchers, we rapidly bring modeling breakthroughs into production and redefine how intelligent systems discover, retrieve, and synthesize information at planetary scale. About the Role We’re looking for a Software Engineer focused on building and scaling retrieval systems. You’ll work with a team of researchers and engineers to develop infrastructure that enables models to retrieve and act on the right information at the right time. This includes designing and operating indexing systems, retrieval pipelines, and serving layers. This work supports retrieval across OpenAI products and research, with direct impact on system performance, reliability, and scale. Responsibilities Build and scale retrieval infrastructure across indexing, serving, and query execution. Develop low-latency, high-throughput systems for real-time model interaction. Partner with research to productionize embedding and retrieval techniques. Support dense, sparse, and hybrid retrieval pipelines. Own system performance, reliability, and observability at scale. Collaborate across Pretraining, Inference, and Product teams to integrate retrieval end-to-e

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 transforming how the world uses data and AI — and the networking and traffic infrastructure that powers these experiences is mission-critical. As a Product Manager focused on Traffic & Networking, you will define how Snowflake delivers secure, reliable, and high-performance connectivity at global scale, including the networking foundations required to support AI-driven products and workloads. You will own the product vision and roadmap for internal traffic management, service-to-service networking, customer connectivity, and performance optimization across multi-cloud environments. A core part of this role is defining and evolving Snowflake’s network strategy to support AI products , including latency-sensitive inference, large-scale model training pipelines, vector search, streaming ingestion, and cross-region data movement. This is a high-impact role at the intersection of distributed systems, cloud networking, and AI infrastructure. AS A PRODUCT MANAGER AT SNOWFLAKE YOU WILL: Define the networking strategy required to support Snowflake’s AI products , including low-latency inference paths, high-throughput data pipelines, GPU-adjacent services, and elastic scaling for AI workloads. Partner with AI platform, compute, and storage teams to ensure networking

Become a part of our caring community The Financial Analytics Professional 2 manages data to support and influence decisions on day-to-day operations, strategic planning and specific business performance issues. The Financial Analytics Professional 2 work assignments are varied and frequently require interpretation and independent determination of the appropriate courses of action. The Financial Analytics Professional 2 collates, models, interprets and analyzes data in order to identify, explain, influence variances and trends. Explains variances and trends in clinical and financial data and enhances modeling techniques to improve forecast accuracy. May possess financial or actuarial background. Understands department, segment, and organizational strategy and operating objectives, including their linkages to related areas. Makes decisions regarding own work methods, occasionally in ambiguous situations, and requires minimal direction and receives guidance where needed. Follows established guidelines/procedures. Use your skills to make an impact Required Qualifications Bachelor's Degree 1&#43; years SQL and Python experience Microsoft Office (Excel, Power BI, etc.) Experience in compiling, modeling, interpreting and analyzing data in order to identify, explain, influence variances and trends Explain variances and trends and enhance modeling techniques Experience in managing data to support and influence decisions on day-to-day operations, strategic planning and specific business performance issues Must be passionate about contributing to an organization focused on continuously improving consumer experiences Preferred Qualifications Business Intelligence, Financial, or Actuarial background Azure Databricks experience</