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At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Lyft Infrastructure builds the systems to ship stable, scalable, and efficient services. We're hiring a Senior Technical Program Manager to lead our AI transformation in developer productivity: rethinking how engineers at Lyft work with AI, from tooling and agentic workflows to the platforms that make them possible. This role blends program delivery with product sense: you'll own the roadmap for your area, set priorities, and act as the voice of the customer. Responsibilities: Lead Lyft's AI transformation in developer productivity: shape the strategy for how AI enhances engineering workflows, from adoption of tools like Cursor, Copilot, and Claude Code to building AI-native workflows and agentic tooling for engineers Own the roadmap: sequence the work, make the prioritization calls, and define what success looks like for AI-driven engineering productivity at Lyft Partner with engineering teams to build AI-native workflows that solve real developer pain, not just deploy tools for the sake of it Run programs end to end, from kickoff through delivery, coordinating across infrastructure, developer platforms, and engineering leadership Be the voice of the customer: partner with engineering teams across Lyft, surface their pain points, and feed that back into priorities and roadmaps Define success metrics and adoption goals, gather and document customer requirements, and make sure what ships actually solves the problem Partner with engineering and infrastructure leads to build plans, call out risks early, and keep stakeholders aligned Own program health: track milestones, surface blockers before they slip, and keep decision-makers in the loop Use your technical background to ask sharp questions and build plans the team believes in Share in the team's release oncall rotation Experience: 5+ years in Technic

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: Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb. The Community Support Products (CSP) Machine Learning team is the core team responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI technologies to enable an intelligent, scalable and exceptional service experience. The team develops and enhances various AI models, ML services and tools including LLM fine-tuning and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. The richness of Airbnb's data, the complexity of its marketplace and the variety innate in our product mean that we need to operate at the state of the art of AI practice. We are committed to investing in long term innovation to solve the complex problems we face, and to do that we need the very best experts in ML and AI to join us. The Difference You Will Make: We believe our current customer experiences in these domains are only scratching the surface of the innovations that are possible, and that science is at the heart of delivering a step-function change for our Guest and and Host on Airbnb. You will build and leverage cutting edge AI technologies to transform Airbnb’s customer service by delivering personalized, easy-to-use and proactive customer service experience. Many of the initiatives you’l

About Us Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X , and discover the projects we’re solving on our Blog . Be sure to explore our Interviewing Guide to learn how to ace our interview process. About the Role Platform Core Engineering (PCE) builds the foundational systems, frameworks, and tooling that Twitch's engineers build on every day. As a Senior Product Manager on PCE, your customers are developers, and your success depends on earning their trust. You will own a platform product area and be measured on whether developers actually adopt, rely on, and advocate for what you ship. This is a deeply technical, senior individual-contributor role. You are expected to be hands-on with modern AI development tools, to understand the technology you are shaping at a real level (not just at the surface), and to hold credibility in a room full of engineers. You can be based out of one of the Twitch offices including San Francisco, Seattle, Los Angeles, Irvine, or New York City. You Will: Own the strategy and roadmap for a PCE platform area, balancing near-term developer needs with a 1 to 2 year technical vision. Treat developers as your customers: understand their workflows, earn their trust, and prioritize the capabilities that make them faster and more effective. Work directly with Senior Engineering Managers and Principal Engineers to define technical and organizational needs and build a cohesive platform ecosystem. Define and drive the platform metrics that matter. Drive alignment across dependent

About Us Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X , and discover the projects we’re solving on our Blog . Be sure to explore our Interviewing Guide to learn how to ace our interview process. About the Team Community Trust (CT) exists to foster an environment where streamers and their communities thrive, while proactively reducing harm and ensuring support is accessible when needed. Our organization encompasses safety policy & enforcement, fraud prevention, and customer experience. Within Customer Trust, the Strategic Intelligence & Governance (SIG) team is the strategic and enablement backbone of the department, where we ensure the business is set up correctly, measure whether it's working, surface insights to drive decisions, and provide the tools that power CT's day-to-day operations. The Business Intelligence function owns CT's data infrastructure, dashboards, financial analysis, and operational reporting, ensuring leadership has the insights they need to make informed decisions across the organization. About the Role As the Business Intelligence Program Manager, you will join a small, high-impact team responsible for CT's data infrastructure, operational reporting, analytical insights, and dashboards. You will translate operational data into clear insights that help leadership and partners make informed decisions across Trust & Safety (T&S) and Customer Experience (CX). You will also be responsible for CT’s financial operations, including operational expenses, work

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-thr

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Flexdrive, Lyft's wholly-owned fleet management subsidiary, is undergoing a transformational shift from traditional rideshare fleet operations to autonomous vehicle fleet ownership and operations. We are seeking an AV Program Manager to help drive the cross-functional operating plan that will prepare Flexdrive to receive, staff, and operate AV fleets at scale domestically and abroad. This role supports the operational backbone of Flexdrive's AV readiness initiative. You will maintain the program timeline, coordinate cross-functional workstreams spanning infrastructure, people, fleet operations, treasury, legal, and finance, and help ensure that requirements are tracked, dependencies are surfaced, and deployment milestones are met. You will work closely with workstream leads across Operations, Strategy, Infrastructure, Finance, Legal, Analytics, and People. If you thrive in ambiguity, can bring structure to complex multi-workstream programs, and have a bias toward action, this role is for you. Responsibilities: 1. Program Planning & Execution Maintain the program timeline for Flexdrive AV Operating Readiness, tracking deliverables and dependencies across workstreams: Strategic Planning, Infrastructure, Analytics, People, Fleet Operations, Treasury, Finance, and Legal. Maintain the program's RAID log (Risks, Assumptions, Issues, Dependencies), surfacing cross-functional blockers with clear context for escalation. Support weekly leadership syncs and monthly deep-dive reviews, including preparation, presentation and follow-through. Translate upstream decisions (partner agreements, market selections, ) into operational requirements, updating the readiness roadmap accordingly. 2. Cross-Functional Coordination Serve as a connection point between workstream leads, helping ensure infrastructure time

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is our evaluation platform — the unified evals backbone that lets teams measure, trace, and trust the quality of LLM and agent systems across the company, powering trace/score ingestion, LLM-as-judge workflows, agent simulations, and LLM observability for the tens of millions of daily requests flowing through our LLM Gateway. We also own core platform surfaces including the Agent Gateway, open-weights model serving and batch inference, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our evals and LLM observability platform: the systems that let teams evaluate, trace, and continuously improve the quality of LLM and agent products. You’ll work across evaluation frameworks and SDKs, OpenTelemetry-based trace/score ingestion, LLM-as-judge and offline/online eval pipelines, agent simulations, data pipelines, backend services, and observability. This role is ideal for an engineer who enjoys building reliable measurement and quality primitives in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and evaluation methodologies are evolving quickly. You’re excited about this opportunity because you will… Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Work on our unified evals platform — evaluation SDKs, OpenTelemetry trace/score ingestion, LLM-as-judge, offline and online eval pipelines, and agent simulations — alongside the LLM Gatew

About the Team The Storage organization builds and operates the online stateful systems and abstractions that DoorDash Engineering depends on: reliable, efficient, secure, and easy to use. Within Storage, the Distributed Caching team owns every caching offering at DoorDash end to end, including ElastiCache (Redis/Valkey), Boulder (our KVRocks-based key-value store for high-QPS feature serving), Entity Cache (read Bill Shen’s engineering blog post, “ High-Performance Proxy Cache for DoorDash Services ”), and the Distributed Lock Service, plus the smart clients (asgard-redis, valkey-go) that sit in front of them. These systems back critical product surfaces across DoorDash, Wolt, and Deliveroo: the team runs roughly 400 ElastiCache clusters serving hundreds of millions of GET requests per second in aggregate, and Boulder, our offline-to-online feature store, serves billions of feature lookups per second at peak. About the Role The team owns provisioning of clusters and the smart clients that sit in front of them, baking in sensible defaults so that other engineering teams get a turnkey caching solution instead of having to run their own. You'll help drive Boulder's evolution to scale further, improve cost efficiency, enhance performance, and support real-time updates; re-platform the Distributed Lock Service onto a strongly consistent backend; and build the self-serve tooling and recommendation engine that let customers describe a workload (QPS, TTL, payload size, latency profile) and get the right backend without talking to a human. You'll go deep on cache invalidation, replication, sharding, compaction, and failover, while shipping the guardrails, automation, and observability that keep this scale operable by a small team. You must be located in San Francisco, Seattle, or the New York Metro Area for this hybrid position. You will report to the Engineering Manager on the Distributed Caching team within the Storage organization. You’re excited about this opportunity b

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Software Engineer on Spark Platform, you will execute across the surfaces of our in-house Spark deployment that serves the entire company. The work spans Spark runtime upgrades and performance, multi-tenant scheduling and executor bin-packing on Kubernetes, cluster lifecycle automation, and the observability and incident automation that keep the platform sustainable. You will move between layers as the work demands — picking up the next high-leverage problem regardless of where it sits — and partner closely with the rest of the team and with platform consumers across the company. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Build and operate an in-house Spark platform that runs at company-wide scale, spanning runtime, scheduler, reliability, and user-facing tooling. Drive multi-tenant scheduling, executor bin-packing, and cost-aware placement that let a small team serve dozens of consumer teams. Own pieces of cluster lifecycle automation — provisioning, upgrades, capacity changes, and node-failure handling — at a scale where these stop being manual events. Build the observability and incident automation that make the platform debuggable end-to-end and keep on-call sus

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The Lifecycle Growth Marketing team engages with millions of Lyft riders and drivers to drive preference and usage of Lyft. The team runs hundreds of acquisition, engagement, retention and resurrection experiments every year across email, push notifications, SMS and in-app messaging surfaces. We are data-driven in everything we do. We test and measure all of our communications and tailor experiences to be personalized and contextual by audience segment. In this role, you will work closely with Product, Engineering, Analytics, Data Science, and Marketing Operations to design, test and optimize incentive and non-incentive messages and experiences. Responsibilities: Own the lifecycle marketing strategy for your audience — define the channel mix, set the prioritization framework, and ensure every communication delivers clear value to the target user Build and manage an experimentation program that drives measurable impact — from ideation through results, with a clear point of view on what to test Partner with Engineering, Product, and Data Science to unlock new platform capabilities, influence roadmaps, and push beyond out-of-the-box tooling Own performance reporting — define metrics, experimentation setup, and share results and next steps with stakeholders and leadership Drive creative strategy — brief, review, and iterate on assets across email, push, SMS and in-app with a strong feedback loop with designers and copywriters Apply behavioral science frameworks to inform message sequencing, timing, and incentive design Experience: 6+ years in growth marketing. You've owned a full lifecycle marketing program from the ground up — not just executing campaigns, but building strategy from high-level goals and managing a prioritized roadmap Strong SQL skills required — you’re comfortabl

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 . The Media Experience team exists to empower users to discover, consume, and engage with the best media content on Reddit. We do that by making video consumption intuitive and engaging. Because video is a powerful medium for community connection, our goal is to ensure our video experience is best-in-class for every user. Our north star is growing video views and video users . These are users who consistently return to Reddit for the unique, authentic content that fuels our communities. Achieving this level of consistent engagement requires a product flywheel that surfaces the right video features, UI optimizations, and playback improvements at the right moment to accelerate organic engagement. We use data, Quality of Experience (QoE) signals, and rapid product experimentation to identify and resolve any friction preventing a seamless viewing experience. We move with urgency, instrument everything, and treat a missed engagement milestone as a signal to learn from, not a number to ignore. If you want to build iOS video experiences that measurably change the trajectory of tens of millions of users and improve the overall engagement of Reddit itself, this is your team. What you’ll do: 🚀 Drive the Technical Strategy for Media Engagement Lead the iOS architecture for user-facing features that boost video discovery and consumption. You’ll design and build immersive viewing surfaces and intuitive UI/UX flows that directly maximize watch time, user retention, and daily active video consumers. 🔬 Champion for User Experience With

About Scale AI At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. Scale Frontier Data is the organization behind the training and evaluation data that frontier labs depend on. We build the systems, tooling, and expert workflows that turn hard human expertise into signals that models can learn from, across reasoning, coding, agentic tool use, and domain expertise. Reinforcement learning environments are now the center of gravity for that work: the difference between a model that demos well and a model that reliably completes long-horizon work is almost always the quality of the environments and reward signals it was trained against. Responsibilities As a Staff Software Engineer, RL Environments, you'll own the technical foundation for how Scale builds, runs, verifies, and delivers RL environments at scale. An RL environment is a real piece of software: a containerized world with real dependencies, real state, real tools, and a grader that has to be correct even when the agent is creative about breaking it. Building one is a full-stack engineering problem. Building thousands of them reproducibly, cheaply, with trustworthy reward signals and throughput measured in millions of rollouts is a systems problem that very few people have solved. You'll work on both. You'll design the platform: sandboxed execution, environment packaging and versioning, rollout orchestration, trajectory capture, verifier frameworks, and the authoring surfaces that let engineers and domain experts produce environments without reinventing infrastructure each time. And you'll go deep on the environments themselves by instrumenting real applications, designing task suites that expose specific capability gaps, and building graders that

Owns day-to-day deal pricing execution for Enterprise, including pricing calculations, staffing inputs, and dashboard reporting that gives Sales and Deal Desk leadership visibility into deal status, pricing, and margin. Partners with the Deal Desk Manager on customer deal structuring and on identifying ways to accelerate the deal approval process. What You’ll Do Deal Pricing Execution Manages day-to-day deal pricing calculations for Enterprise deals, including ROI modeling and EPD staffing inputs Applies existing pricing frameworks consistently across Land, Expansion, and Framework deals Partners with the Deal Desk Manager to design customer-specific deal structures for non-standard or complex deals Pricing & Deal Status Reporting Builds and maintains a pricing dashboard tracking deal status, pricing, margin, and overall deal health across the Enterprise pipeline Updates pricing and trend reporting to keep leadership current on where deals stand and how pricing is trending against targets Process Optimization Identifies bottlenecks in the deal review & approval process and proposes ways to accelerate cycle time Helps standardize and streamline recurring pricing/approval steps into repeatable, lower-friction workflows Tracks deal approval cycle time and surfaces trends to inform process improvements Cross-Functional Deal Coordination Owns deal crafting end-to-end from initial pricing/modeling through contract review, coordinating with sales management, reps, and internal stakeholders Ensures the relevant stakeholders (Accounting, Finance, EPD, Legal) review and approve deals at the appropriate stage before they progress Routes deal packages to the correct reviewers and tracks open items so deals aren't stalled waiting on sign-off Strategic & GTM Support Tackles ambiguous, open-ended questions around Scale's go-to-market motion and iterates quickly on solutions that deliver measurable results Supports Sales and Finance leadership in quarterly strategy and

Scale GP is Scale's enterprise Generative AI platform—APIs and infrastructure for knowledge retrieval, inference, evaluation, and intelligent automation. We power mission-critical workflows for leading enterprises, helping teams turn complex data and models into reliable, production-ready AI systems. We're building a new AI Enablement team to create the next generation of agent-powered tools that ground AI in real operational workflows. Our goal: help internal teams demystify their own workflows, then deploy agentic systems that reason over data, take action, and deliver measurable outcomes. We don't build in a vacuum. You'll use our own platform to solve real business problems internally—then selectively commercialize that same stack for customers. What we run on is what we sell. This is a 0→1 team. We're looking for a sharp, product-minded engineer who thrives in ambiguity, moves fast, and loves building systems from scratch alongside customers and cross-functional partners. You'll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable production solutions. If you like shipping fast, owning outcomes, and working across the stack—from polished frontends to distributed backends to LLM integrations—this role is for you. What You’ll Do Own full-stack features and projects end-to-end — from design through production deployment — within a larger product area Sample surfaces - Accounting Agents, Finance Copilots, GTM Agents, Agentic Experimentation Platforms Develop reliable backend services in Typescript/Python, work with distributed systems, data pipelines, and AI/ML infrastructure Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows Ship quickly through tight experimentation loops while maintaining high quality and reliability Adapt across the stack and learn new tools as needed to solve real problems end-to-end Ideal Experience 3+ years of full-tim

About the Team The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers. About the Role The Storage team is building and operating a high-performance, scalable, and reliable data abstraction layer that optimizes both efficiency and reliability. Our goal is to create a platform that manages itself and fades into the background—empowering engineers to focus on delivering product experiences our customers love. This role is available across two teams within Storage, each solving unique and high-impact challenges: One team is building the orchestration layer for DoorDash’s storage platform—unifying lifecycle management, operations, and self-serve APIs for databases and streaming systems, turning complex, stateful infrastructure into reliable, developer-friendly services used across the company. One team builds and operates the distributed data platform powering DoorDash's largest stateful workloads -- including Cassandra, which backs critical product surfaces across DoorDash, Wolt, and Roo. You'll design high-throughput data abstractions, smart clients, and platform services that make distributed data reliable and easy to work with at multi-petabyte, multi-million-QPS scale, with opportunities to go deep on distributed systems internals and contribute to the open-source Cassandra ecosystem. If you're passionate about distributed systems, developer experience, and building foundational infrastructure at scale, we'd love to hear from you. You must be located in San Francisco, Sunnyvale, Seattle, or the New York Metro Area for this hybrid pos