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 Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning. The ML Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure. What you’ll do You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities - Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. - Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. - Working directly with product teams and ML engineers to improve their day-to-day productivity. - Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies. Who you are We’re looking for people with a strong background or interest in building successful products or systems; you’re passionate about solving business problems and making impact, you are comfortable in dealing with lots of moving pieces; and you’re comfortable learning new technologies and systems. You are comfortable working with other Stripe teams across the US and Canada. Minimum requirements - 2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems - Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations - Experience working on production ML platforms, MLOps solutions, or building LLM applications - Experience running operations for high availability, low latency systems - Experience partnering with other teams to drive business outcomes - A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course Preferred qualifications - Experience building and shipping production AI agents - Familiarity with the LLMs and LLM Frameworks - Experience training and shipping machine learning models to production to solve critical business problems

• Architect, design, and implement a production-grade lane and route network mapping stack, ensuring high-performance integration with the broader autonomy system. • Lead the research, design, and training of advanced neural architectures. This includes vectorized mapping networks (e.g., MapTR), multi-camera BEV transformers, and LiDAR-camera fusion models to extract and model lane and route networks for offline and online mapping. • Lead major feature development from inception to deployment. This includes high-level architecture design, rigorous code reviews, automated testing, and technical resolution. • Own the end-to-end data strategy for the mapping domain. You will define data curation, auto-labeling, synthetic data, and active learning pipelines to capture and resolve long-tail scenarios. • Develop robust metrics and evaluation frameworks for lane and route network accuracy, temporal consistency, and scaling across diverse Operational Design Domains (ODDs). • Work independently with cross-functional teams to translate complex autonomy goals into clear software and system requirements. • Collaborate with ML and Autonomy engineers to ensure the seamless deployment and validation of mapping features to the vehicle fleet. • Stay at the research frontier by evaluating, adapting, and innovating cutting-edge techniques. This includes online vectorized HD map construction, end-to-end mapping models, and vision/fusion foundation models to deliver production-ready solutions.

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview Instacarts Data Infrastructure organization builds and operates the systems that power our company’s data ecosystem, including a modern open data lakehouse on Apache Iceberg, a multi-engine compute platform for stream and analytical workloads, and self-serve tooling that helps Product, Data Science, ML, Ads, Finance, and engineering teams move fast with data. We’re looking for a Staff Software Engineer, Data Infrastructure to join our Data Governance and Foundations Team. In this role, you’ll serve as a senior technical leader owning the architecture and delivery of our open lakehouse foundation, governance and access patterns, and multi-engine compute strategy—balancing today’s reliability with the next three to five years of scale, maturity, and cost efficiency. You’ll collaborate closely with engineering leadership and stakeholders across Data Science, ML Platform, Ads Infrastructure, Finance Engineering, Product Engineering, and Security. You’ll operate with a high degree of ownership in a fast-paced environment where architectural decisions have real technical and financial consequences. If you thrive on complex, high-scale challenges and roll-up-your-sleeves execution, this is a chance to shape the backbone of Instacart’s data platform. Our stack includes technologies such as Apache Iceberg, Apache Flink, Trino, ClickHouse, Apache Kafka, Apache Spark, Snowflake, Databricks, Confluent, Airflow, dbt, Delta Lake, Scala, Python, Postgres, and AWS. You’ll join a focused team of 7 engineers that values pragmatism, clarity, and impact. About the Job - Translate Instacart’s data strategy (e.g., monetization, federated access, real-time) into an actionable multi-year architecture roadmap; align with leadership while evolving the platform for scale, maturity, and cost efficiency. - Own the open lakehouse foundation: define and deliver unified table formats, storage governance, and a multi-engine compute portfolio (interactive, batch, streaming) that enables portability and prevents lock-in. - Drive real-time and streaming infrastructure for critical use cases (Ads, Fraud, ML): set deployment patterns, SLAs, and operational practices that balance performance, availability, and spend. - Pioneer AI-native data infrastructure engineering by applying LLM/AI tools to the platform lifecycle—accelerating development, automation, observability, and cost optimization—and partnering to embed AI-powered capabilities into the platform. - Elevate engineering excellence: lead architecture reviews, mentor senior/staff engineers, influence hiring, and clearly communicate complex trade-offs to both technical and executive audiences to ensure cross-org alignment. About You Minimum Qualifications - 5+ years of software engineering experience building and operating data infrastructure or distributed systems at production scale. - Hands-on expertise with modern data lakehouse architectures and open table formats (e.g., Apache Iceberg, Delta Lake, Hudi) and with distributed query/compute engines (e.g., Trino, Spark, ClickHouse), including performance tuning and production reliability. - Experience with event-driven and streaming infrastructure (e.g., Kafka, Flink) for real-time pipelines and serving systems. - Proven ownership of major platform transitions or migrations (build vs. buy, migration design, risk management) delivered to production. - Ability to build cost/benefit and TCO models for infrastructure investments and to drive alignment via clear architecture docs and strategy memos across multiple teams and leadership levels. Preferred Qualifications - Experience designing platform-level governance controls and familiarity with compliance frameworks (e.g., SOX, CPRA, GDPR). - FinOps experience optimizing data platform spend, including managing multi-million dollar infrastructure budgets and negotiating vendor contracts. - Deep SQL proficiency and strong skills in Python or Scala for systems-level development. - Experience with orchestration (e.g., Apache Airflow) and data transformation pipelines (e.g., dbt) in large-scale production environments. - Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience. #LI-Remote Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here. Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here. For US based candidates, the base pay ranges for a successful candidate are listed below. CA, NY, CT, NJ $265,000—$279,500 USD WA $254,000—$268,000 USD OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $243,000—$256,500 USD All other states $221,000—$233,000 USD

Role Description You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities - Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. - Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. - Working directly with product teams and ML engineers to improve their day-to-day productivity. - Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies. Qualifications - 2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems. - Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations. - Experience working on production ML platforms, MLOps solutions, or building LLM applications. - Experience running operations for high availability, low latency systems. - Experience partnering with other teams to drive business outcomes. - A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course. Preferred Qualifications - Experience building and shipping production AI agents. - Familiarity with the LLMs and LLM Frameworks. - Experience training and shipping machine learning models to production to solve critical business problems.