Software Engineer Data Infrastructure Jobs

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . As a Senior Staff Software Engineer on the Data Platform team within Platform , you'll define and lead the technical strategy for Coinbase's data infrastructure, spanning ingestion, transformation, warehousing, streaming, and serving systems. This is a foundational role at the intersection of distributed systems, data engineering, and AI-readiness, reporting to the Senior Director of Engineering. You'll set architectural direction, drive multi-quarter roadmaps, and transition the organization from managed-service dependency toward engineering-built, platform-grade infrastructure that powers everything from fraud detection to modern multi-agent AI architectures. What you'll do: Own the technical strategy and architecture for Data Platform, setting direction across data ingestion, transformation, warehousing, streaming, and serving systems while driving engineering-led cost reduction at the infrastructure layer. Architect data infrastructure to natively support AI and ML workloads, ensuring pipelines, data lake systems, and compute can power ML training, feature stores, real-time inference, and multi-agent AI architectures at scale. Drive the evolution to near-real-time data availability, enabling downstream teams across Coinbase to act on fresher data for fraud detection, financial reporting, and analytics. Build alignment and secure commitment from senior leadership

About the Team Data Platform at OpenAI owns the foundational data stack powering critical product, research, and analytics workflows. We operate some of the largest Spark compute fleets in production; design, and build data lakes and metadata systems on Iceberg and Delta with a vision toward exabyte-scale architecture; run high throughput streaming platforms on Kafka and Flink; provide orchestration with Airflow; and support ML feature engineering tooling such as Chronon. Our mission is to deliver reliable, secure, and efficient data access at scale and accelerate intelligent, AI assisted data workflows. Join us to build and operate these core platforms that underpin OpenAI products, research, and analytics. We’re not just scaling infrastructure – we’re redefining how people interact with data. Our vision includes intelligent interfaces and AI-assisted workflows that make working with data faster, more reliable, and more intuitive. About the Role This role focuses on building and operating data infrastructure that supports massive compute fleets and storage systems, designed for high performance and scalability. You’ll help design, build, and operate the next generation of data infrastructure at OpenAI. You will scale and harden big data compute and storage platforms, build and support high-throughput streaming systems, build and operate low latency data ingestions, enable secure and governed data access for ML and analytics, and design for reliability and performance at extreme scale. You will take full lifecycle ownership: architecture, implementation, production operations, and on-call participation. You’ve supported Spark, Kafka, Flink, Airflow, Trino, or Iceberg as platforms. You’re well-versed in infrastructure tooling like Terraform, experienced in debugging large-scale distributed systems, and excited about solving data infrastructure problems in the AI space. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per wee

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? The Data Infrastructure team at Cohere is responsible for the storage and data movement layer underlying every model training run. We're building the unified storage layer that feeds our training workloads. It needs to serve petabytes of training data and model checkpoints fast enough to keep thousands of GPUs busy across several training clusters. In this role, you’d have an opportunity to build this system from the ground up. You’d be a key contributor, working on a problem few teams have had to solve at this scale. In this role, you will: Design, build, and operate the distributed storage system that feeds model training and evaluation. Run this system multiple on Kubernetes clusters at petabyte scale. Work with researchers and training-infra teams on how jobs actually read and write data, and turn that into throughput, latency, and durability requirements Work through the networking, I/O, and consistency problems of moving large datasets and checkpoints across regions and backends, with GPU idle time and time-to-insight as the measures of success You may be a good fit if you have: Strong storage fundamentals,

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 data infrastructure behind some of the most demanding AI training workloads in the world, and we want sharp, curious people to help us do it. In this role, you'll build and maintain the high-performance data layer our Modeling teams rely on for training and evaluation jobs. As a Software Engineer, Data Infrastructure, you will: Work directly on petabyte-scale storage infrastructure, and the networking and performance challenges that come with it. Collaborate daily with researchers and engineers who are some of the best in the world at what they do. You may be a good fit if you have: 4+ years of experience working on data storage infrastructure Strong command of Python Kubernetes experience, especially on the storage side (Persistent Volumes, CSI drivers, etc.) The ability to transform unstructured data into performant datasets across diverse storage backends including S3, GCS, and POSIX Experience with distributed data processing frameworks such as Apache Beam, Spark, or Flink [Nice-to-have] Familiarity with modern analytics tooling such as BigQuery, Airflow, or dbt Genuine excitement about AI.

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors About the Role Nuro takes a machine-learning-first approach to autonomous driving technology. In an ML-first system, the overall system performance depends heavily on the quantity and diversity of its training and evaluation data. The team plays a crucial role in the advancement of autonomous driving systems by creating a scalable and reliable data infrastructure. This infrastructure is designed to produce training and evaluation data derived from both on-road collected logs and simulation logs. Additionally, the team collaborates closely with system engineers to thoroughly validate the autonomous driving system before its deployment. About the Work Design and develop unified, introspectable, large-scale batch and streaming data pipelines that can ingest and process data across a wide range of use cases relevant to evaluation. Create and implement a storage system capable of accommodating both the large volume and diverse range of e

About the Team The Workload team is responsible for designing and running OpenAI’s LLM training and inference infrastructure that powers frontier models at massive scale. Our systems unify how researchers train and serve models, abstracting away the complexity of performance, parallelism, and execution across vast GPU/accelerator fleets. By providing this foundation, the Workload team ensures that researchers can focus on advancing model capabilities while we handle the scale, efficiency, and reliability required to bring those models to life. About the Role We are looking for an engineer to design and implement the dataset infrastructure that powers OpenAI’s next-generation training stack. You will be responsible for building standardized dataset interfaces, scaling pipelines across thousands of GPUs, and proactively testing performance bottlenecks. In this role, you will collaborate closely with the multimodal researchers, and other infra groups to ensure datasets are unified, efficient, and easy to consume. In this role, you will: Design and maintain standardized dataset APIs, including for multimodal (MM) data that cannot fit in memory. Build proactive testing and scale validation pipelines for dataset loading at GPU scale. Collaborate with teammates to integrate datasets seamlessly into training and inference pipelines, ensuring smooth adoption and a great user experience. Document and maintain dataset interfaces so they are discoverable, consistent, and easy for other teams to adopt. Establish safeguards and validation systems to ensure datasets remain reproducible and unchanged once standardized. Debug and resolve performance bottlenecks in distributed dataset loading (e.g., straggler systems slowing global training). Provide visualization and inspection tools to surface errors, bugs, or bottlenecks in datasets. You might thrive in this role if you: Have strong engineering fundamentals with experience in distributed systems, data pipelines, or infrastructure.

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors About the Role Nuro takes a machine-learning-first approach to autonomous driving technology. In an ML-first system, the overall system performance depends heavily on the quantity and diversity of its training and evaluation data. The team plays a crucial role in the advancement of autonomous driving systems by creating a scalable and reliable data infrastructure. This infrastructure is designed to produce training and evaluation data derived from both on-road collected logs and simulation logs. Additionally, the team collaborates closely with system engineers to thoroughly validate the autonomous driving system before its deployment. About the Work Design and develop unified, introspectable, large-scale batch and streaming data pipelines that can ingest and process data across a wide range of use cases relevant to evaluation. Create and implement a storage system capable of accommodating both the large volume and diverse range of e

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors About the Role Nuro takes a machine-learning-first approach to autonomous driving technology. In an ML-first system, the overall system performance depends heavily on the quantity and diversity of its training and evaluation data. The team plays a crucial role in the advancement of autonomous driving systems by creating a scalable and reliable data infrastructure. This infrastructure is designed to produce training and evaluation data derived from both on-road collected logs and simulation logs. Additionally, the team collaborates closely with system engineers to thoroughly validate the autonomous driving system before its deployment. About the Work Design and develop unified, introspectable, large-scale batch and streaming data processing systems that can ingest and process data across a wide range of use cases relevant to evaluation. Create and implement a storage system capable of accommodating both the large volume and diverse r

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors About the Role Nuro takes a machine-learning-first approach to autonomous driving technology. In an ML-first system, the overall system performance depends heavily on the quantity and diversity of its training and evaluation data. The team plays a crucial role in the advancement of autonomous driving systems by creating a scalable and reliable data infrastructure. This infrastructure is designed to produce training and evaluation data derived from both on-road collected logs and simulation logs. Additionally, the team collaborates closely with system engineers to thoroughly validate the autonomous driving system before its deployment. About the Work Design and develop unified, introspectable, large-scale batch and streaming data processing systems that can ingest and process data across a wide range of use cases relevant to evaluation. Create and implement a storage system capable of accommodating both the large volume and diverse r

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team The Big Data Infrastructure operates the critical infrastructure that powers the batch data processing at Stripe. The team supports a variety of use cases, including Payment, Ledger, ML, Fraud Detection, Product Analytics, Regulatory Reporting, Financial Data Reconciliation, and externally facing products like Radar and Sigma. As an example of the scale, the team's systems serve hundreds of teams, thousands of workflows, 100,000+ task executions, O(billion) transformations, and moving terabytes of data processing over 1 GB/second every day. Our users inside Stripe include other engineering teams, Data Scientists, Sales and Operations, Finance, etc. Data Orchestration builds and operates the time-based and event-based orchestration infrastructure that powers and accelerates batch data pipelines. The team operates on a wide range of tech stacks including Airflow, Spark, SQL, Kafka, Flink, Hive MetaStore, Trino, Pinot, Python, Java, Scala, S3, and Iceberg. What you'll do As a Software Engineer on this team, you'll design and build infrastructure that powers batch data processing at Stripe. Responsibilities Design, build, and maintain next-generation and first-generation versions of key Data Platform products, with an emphasis on usability, reliability, security, and efficiency. Design ergonomic APIs and abstractions that build a great customer experience for internal Stripes, that will in turn enhance the experience of millions of Stripe users.

About the Team Data is at the foundation of DoorDash success. The Data Engineering team builds database solutions for various use cases including reporting, product analytics, marketing optimization and financial reporting. By implementing pipelines, data structures, and data warehouse architectures; this team serves as the foundation for decision-making at DoorDash. About the Role DoorDash is looking for a Softare Engineer II to be a technical powerhouse to help us scale our data infrastructure, automation and tools to meet growing business needs. You're excited about this opportunity because you will… Work with business partners and stakeholders to understand data requirements Work with engineering, product teams and 3rd parties to collect required data Design, develop and implement large scale, high volume, high performance data models and pipelines for Data Lake and Data Warehouse Develop and implement data quality checks, conduct QA and implement monitoring routines Improve the reliability and scalability of our ETL processes Manage a portfolio of data products that deliver high-quality, trustworthy data Help onboard and support other engineers as they join the team We're excited about you because… 3+ years of professional experience working in data engineering, business intelligence, or a similar role You have proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Proficiency in programming languages such as Python/Java 3+ years of experience in ETL orchestration and workflow management tools like Airflow, Flink, Oozie and Azkaban using AWS/GCP Expert in Database fundamentals, SQL and distributed computing 3+ years of experience with the Distributed data/similar ecosystem (Spark, Hive, Druid, Presto) and streaming technologies such as Kafka/Flink. Experience working with Snowflake, Redshift, PostgreSQL and/or other DBMS

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. The Data Clean Rooms team is Leading the market shift from traditional 2-party data sharing to multi-party collaboration hubs . Our vision is to provide a seamless, "safe-room" environment where enterprises can collaborate on shared datasets while maintaining absolute governance. We ensure that no party can exfiltrate another's underlying content, even while running complex joint workloads and getting high-value results. You will join a fast-paced, collaborative team of engineers on a journey to provide customers with an integrated set of innovative, AI-enabled capabilities to analyze data in a privacy-preserving way. You will have a real opportunity to impact and shape the future of secure data collaboration at Snowflake. AS A SOFTWARE ENGINEER IN DATA CLEAN ROOMS, YOU WILL: Architect and build highly scalable infrastructure that enables secure, multi-party collaboration. Design and implement core clean room features and services, intelligent agents, and robust developer APIs to expand platform capabilities and support custom AI/ML workflows. Partner closely with Product Management and cross-functional teams to drive complex projects from ideation and system design through to production deployment. Mentor peers and foster a warm, supportive culture of innovation, cross-tea

Airtable is the no-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable to transform how work gets done. At Airtable, we're passionate about democratizing software creation — empowering anyone to build powerful, flexible tools without writing code. With our shift to an AI-native platform, customers can now generate full apps and deploy AI agents directly into their workflows. Data engineering plays a critical role in this evolution by delivering the insights our teams rely on to improve user experience, measure agent impact, and understand how the business is performing at scale. As a Software Engineer, Data at Airtable, you'll make an enormous contribution to our data engineering efforts. You'll design and own mission-critical data pipelines to enable decision-making, partner with company leaders to create scalable data solutions, and launch innovative alerting and visualization solutions. The Team Our team is embedded in how Airtable understands itself as a business, working closely with Data Science and Analytics. What's unusual here: the platform you're instrumenting is the same one your customers use every day. When Airtable ships a new AI agent capability, you're among the first to wire it up, measure its adoption, and influence what gets built next. Product & AI Data Infrastructure This team builds and owns the foundational data pipelines that power product analytics across Airtable. As Airtable shifts to an AI-native platform, our work increasingly involves instrumenting and measuring AI product usage, building event pipelines for AI agents, surfacing AI-native adoption metrics in core business tables, and developing AI-powered data discovery tooling, including vector search over our catalog metadata. We partner closely with product analytics, product engineering, and data infrastructure to turn business question

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. The Data Access team within Infra / Storage builds EaaS (Entities-as-a-Service), Roblox’s large-scale managed OLTP access and management platform powering tens of millions of QPS across thousands of services. EaaS abstracts the complexity of distributed databases and caching systems behind a consistent and productive developer experience, enabling teams across Roblox to safely build and operate stateful systems at massive scale without requiring deep database expertise. The team operates at the intersection of distributed systems engineering, reliability engineering, and platform architecture — solving hard infrastructure problems that directly impact Roblox-wide scale and stability. As a Senior Software Engineer you will work on some of Roblox’s hardest backend infrastructure challenges around scalability, reliability, workload governance, adaptive flow control, and distributed systems. You Will Build and evolve EaaS, Roblox’s managed OLTP access platform powering tens of millions of QPS across hundreds of services. Design infrastructure that abstracts distributed databases and caching systems behind a consistent, safe, and highly productive developer experience. Drive reliability and scal

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. We are building the next-generation data and storage infrastructure to solve some of the hardest problems in AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Our work gives NVIDIA teams the foundational capabilities they need to build, train, deploy, and operate AI products at scale without reinventing critical data infrastructure for every workload. What you will be doing: Build cloud-native data and storage services for hybrid and multi-cloud infrastructure, including dataset discovery, ingestion, governance, checkpointing, observability, and low-latency access. Develop scalable cloud-native services and APIs that support exabyte-scale, high-performance GPU training and inference workflows. Work closely with product managers, internal AI teams, platform teams, and partner engineering teams to understand requirements and turn them into reliable production systems. Collaborate with SRE, operations, and support teams to improve service reliability, performance, observability, on-call readiness, and operational scale. Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, and verification. What we need to see: BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience, with 5+ years of software engineering experience. Strong foundation in algorithms, data structures, distributed systems, and practi