Staff+ Software Engineer, Experimentation

About the Team The Decision Systems team helps DoorDash teams make faster, more rigorous, and more trustworthy decisions. We build the analytical systems, statistical methods, and product experiences that power experimentation, causal inference, rollout analysis, metric interpretation, and business impact measurement across DoorDash’s three-sided marketplace. Curie is DoorDash’s experiment analysis product within Decision Systems. It sits at the center of how teams move from hypothesis to analysis to confident action, helping decision makers understand experiments, rollouts, metric movements, and marketplace impact while preserving scientific rigor, velocity, trust, and interpretability. Our work spans experimentation platforms, causal inference, variance reduction, switchback experimentation, metric systems, and AI-assisted analysis workflows. We partner closely with data scientists, engineers, product managers, and operators across DoorDash to translate advanced methods into reliable, scalable systems that improve decision quality and product velocity. Some of the work done in the team was published in articles such as: Design-Aware Variance Reduction for Switchback Experiments: A Comparative Study , Power-Optimal Covariate Adjustment for Switchback Experiments , CUPED on Steroids: Multivariate Covariate Adjustment for Switchback Experiments . About the Role We are looking for a Staff Applied Scientist to provide scientific leadership for DoorDash’s Experimentation Platform. In this role, you will define and advance the statistical, causal inference, machine learning, and AI capabilities that power high-quality decision-making across DoorDash. You will work on challenging problems at the intersection of experimentation, marketplace dynamics, machine learning, and product engineering. Your work will shape how DoorDash teams design experiments, analyze results, interpret noisy marketplace outcomes, and make faster, more trustworthy decisio

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's Data Authoring Platform exists to turn raw data into decision-grade insight, fast, reliable, and at scale. We provide a single, paved path for 4,000+ internal data producers and consumers to model, transform, and serve trustworthy data and metrics that power everything from A/B experimentation to executive dashboards. What we own: Minerva, Airbnb's semantic layer & metrics platform , ensuring one definition of truth across the company. The end-to-end data-authoring framework that cuts through pipeline boilerplate and accelerates iteration, pipeline testing, and validation. Tooling for reliability, governance, and cost-efficiency, including automated testing, anomaly detection, and right-sized materialization. The difference you will make As a Staff Software Engineer focused on Minerva, you will own and drive high-impact workstreams end to end. You'll take ambiguous problem spaces, such as making experimentation faster and easier, talk to users, define the work, and deliver it. Your work will help thousands of internal users define, understand, and consume trustworthy business metrics across product, finance, BI, and experimentation. Engineers, designers, and product leaders rely on these tools to explore and develop new product experiences and to run experiments that tell them what is right for our customers. Depending on your background, you may partner closely with the experimentation team to find gaps and shape how Minerva serves them. We believe data is the voice of our customers at scale. The tools you build will help improve the Airbnb product for every guest and h

About the Role: As a Staff Software Engineer on the ML Infrastructure team, you will collaborate closely with the Machine Learning and Product teams to build world-class machine learning inference platforms. These platforms power essential services like personalized recommendations, search, and content understanding across Tubi. A core responsibility of this team is developing and maintaining low-latency ML model serving systems that support Deep Learning, LLM, and Search models. This involves building self-service infrastructure and critical components such as the inference engine, feature store, vector store, and experimentation engine. You will improve the way we deploy and operate our services and even contribute to open-source projects. This role grants the architectural freedom to explore new frameworks, lead critical cross-functional projects, and transform the capabilities of our ML and Product teams. Responsibilities: Design and build scalable, high throughput, and low latency distributed systems using Scala Build reusable components and services that serve various ML applications like Personalization, Search, Ads and Exploration Partner closely with ML engineers to understand their challenges and limitations and develop scalable solutions to address them. Proactively recommend solutions to keep our ML Inference stack state of the art. Take a data driven approach to identifying & optimizing latency, cost, and efficiency of our infra. Lead large scale cross functional refactorings if necessary Mentor other engineers on the team on system design, effective incident management, interviewing, leveraging LLMs for work, etc. Collaborate with ML, Product, and cross functional engineering teams to define the long term vision and architecture for ML Infrastructure at Tubi. Your Background: Experience designing and building scalable, distributed systems in any modern backend language (e.g., Scala, Java, Python, Go, C++); experience with Scala or JVM b

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. App Infrastructure is the team behind Affirm's shared mobile app infrastructure — the platform every other mobile feature team builds on. We own the app’s shared components: design system, networking, observability, experimentation, CI/CD, app releases, developer tooling, availability/stability, and more. We care about ownership: shipping tools and runbooks other teams actually adopt, writing specs before building, and being the go to experts for the mobile tech stack . We're small, technical, and highly collaborative. What You'll Do You will be responsible for setting technical strategy for your team on a year-long time scale, and help your team tie it together with critical, business-impacting projects. You will collaborate across teams in the product development lifecycle by collaborating with product management, design & analytics to ensure technical sustainability, risks and trade-offs are well understood and managed. You will act as a force-multiplier for your team through your definition and advocacy of technical solutions and operational processes. You take ownership of your team’s operations and availability by ensuring you have the right monitoring, triage rotations, playbooks, policies, testing and alerting in place to support “keep the lights on” & on-call efforts. You will foster a culture of quality and ownership on your team by setting code review and design standards for your team, and advocating for them beyond your team through your writing and tech talks. You will help develop talent on your team by providing feedback and guidance, and leading by example. What We Look For You have 7+ years of experience of developing and architecting mobile applications; 4+ using React Native You are proficient in working with Javascript/Typescript. You have hands-on experie

The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, enabling customers to ship AI with confidence. As a Staff Engineer, you’ll lead the development of new features and foundational capabilities within Datadog’s LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production. Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Drive design and implementation of LLM observability features. Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit Develop and extend tools for tracing, evaluating, and debugging LLMs Influence architecture decisions and mentor engineers to build resilient, high-performance systems Stay close to customer pain points and use those insights to guide product and engineering priorities Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering or r

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. App Infrastructure is the team behind Affirm's shared mobile app infrastructure — the platform every other mobile feature team builds on. We own the app’s shared components: design system, networking, observability, experimentation, CI/CD, app releases, developer tooling, availability/stability, and more. We care about ownership: shipping tools and runbooks other teams actually adopt, writing specs before building, and being the go to experts for the mobile tech stack . We're small, technical, and highly collaborative. What You'll Do You will be responsible for setting technical strategy for your team on a year-long time scale, and help your team tie it together with critical, business-impacting projects. You will collaborate across teams in the product development lifecycle by collaborating with product management, design & analytics to ensure technical sustainability, risks and trade-offs are well understood and managed. You will act as a force-multiplier for your team through your definition and advocacy of technical solutions and operational processes. You take ownership of your team’s operations and availability by ensuring you have the right monitoring, triage rotations, playbooks, policies, testing and alerting in place to support “keep the lights on” & on-call efforts. You will foster a culture of quality and ownership on your team by setting code review and design standards for your team, and advocating for them beyond your team through your writing and tech talks. You will help develop talent on your team by providing feedback and guidance, and leading by example. What We Look For You have 7+ years of experience of developing and architecting mobile applications; 4+ using React Native You are proficient in working with Javascript/Typescript. You have hands-on experie