• Partner with platform leadership to evolve the internal developer platform toward an AI-first model • Translate strategic direction into practical, scalable implementations • Help shape platform capabilities through hands-on engineering and technical input • Design and implement self-service infrastructure systems that abstract away cloud complexity • Build APIs, CLIs, and developer-facing tools that enable infrastructure provisioning without requiring deep cloud expertise • Introduce AI-assisted interfaces (CLI, chat, UI) for provisioning, debugging, and operations • Design, build, and operate secure, multi-tenant Amazon EKS clusters • Contribute to platform architecture, scalability, cost optimization, and reliability • Implement policy, networking, and workload isolation best practices • Build systems that interpret infrastructure signals (logs, metrics, events) and provide actionable insights • Develop AI-assisted workflows for incident triage, root cause analysis, and runbook generation/execution • Enable human-in-the-loop automation for safe and controlled remediation • Build platform SDKs, CLIs, and service templates that standardize infrastructure consumption • Contribute to AI-powered copilots and assistants that improve developer productivity • Enable natural language interaction with platform services where appropriate • Implement reusable infrastructure patterns and opinionated defaults • Encode security, scalability, and compliance into platform tooling • Extend Infrastructure as Code practices (e.g., OpenTofu) with abstraction and validation layers • Implement logging, metrics, and tracing across platform systems • Leverage AI to detect anomalies, summarize system behavior, and surface actionable insights • Contribute to systems that move from visibility to guided action • Contribute to platform instrumentation and feedback loops • Use data and developer feedback to improve usability, performance, and adoption • Continuously refine platform capabilities based on real-world usage • Work closely with application teams, SREs, and security engineers • Support platform adoption through strong technical partnership • Provide mentorship and guidance to engineers where needed

Title: Data and ML Engineer Location: Canada or United Kingdom, Remote Department: Engineering Job Description: Remote | some EST overlap preferred Mission Help LaunchGood build scalable data systems and intelligent workflows that power better decision-making across the organization. This role sits at the intersection of Data Engineering, Analytics, and Machine Learning. You’ll help improve our data infrastructure, support self-serve analytics initiatives, and contribute to AI and ML-powered solutions across internal products and workflows. You’ll work closely with the Data team and cross-functional stakeholders to help modernize and scale our data ecosystem. Expectations and Responsibilities Data Engineering & Infrastructure - Build and maintain reliable data pipelines and workflows - Support improvements to data architecture, modeling, and warehouse organization - Help improve data quality, monitoring, observability, and documentation practices - Optimize queries, transformations, and warehouse performance - Support analytics engineering workflows and reusable data models ML & AI Enablement - Assist in building and operationalizing ML and AI-enabled workflows - Support predictive analytics and automation initiatives - Contribute to experimentation with LLM-powered tools and internal AI workflows - Help build scalable datasets and features for ML use cases Collaboration & Execution - Work with Product, Engineering, and business teams to support reporting and analytics needs - Translate business problems into technical solutions - Participate in code reviews, QA, and engineering best practices - Contribute to documentation and knowledge-sharing across the Data team Competencies - 2–4 years of experience in Data Engineering, Analytics Engineering, ML Engineering, or related roles - Strong SQL and Python skills - Experience working with cloud data warehouses such as BigQuery - Exposure to ML models or AI-powered workflows - Familiarity with LLMs, embeddings, or AI tooling - Familiarity with dbt or similar transformation frameworks - Experience building or maintaining data pipeline - Understanding of data modeling and analytics engineering concepts - Familiarity with APIs and automation workflows - Strong problem-solving and communication skills - Comfortable working in fast-moving and evolving environments Nice to Have - Experience with analytics tools such as PostHog, Heap, or Looker - Experience with orchestration or workflow automation tools - Startup or high-growth company experience - Interest in data quality, governance, and self-serve analytics

Role Description The Platform team is part of the Marketplace engineering: we provide specialized business platforms supporting Marketplace ML and Optimization products. We own and host core ML models used throughout the Marketplace; we automate all parts of the model life cycle, such as feature engineering, training workflows and inference services; we are working on next-generation Marketplace Simulation system capable to run high-fidelity virtual experiments; we also streamline diverse Marketplace Experimentation practices with the goal of providing unified user-friendly Experimentation platform. - Implement and scale the core economic engine for Waymo's ride-hailing commercial services. - Work on novel experimentation, simulation and analysis infrastructure supporting Marketplace product data science. Qualifications - BS degree in Computer Science or equivalent practical experience. - 4+ years of experience programming in backend coding languages such as Java or C++. - Experience in building backend platforms supporting multiple product use-cases/services. - Experience working with product data science teams and developing code to automate ML/Optimization product launches. Requirements - MS in Computer Science, or equivalent practical experience. - Experience building and deploying high-fidelity simulation systems running ML/Optimization production code. - Experience developing and configuring Experimentation services running concurrent A/B or Switchback experiments. - Experience working at another Ride hailing or Marketplace company. - Coursework background in experiment design or simulation systems. Benefits - Waymo employees are eligible to participate in Waymo’s discretionary annual bonus program. - Equity incentive plan. - Generous Company benefits program, subject to eligibility requirements. Salary Range The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Salary Range: $175,000 — $215,000 USD

• Lead the engineering organization for data and AI/ML platform • Develop petabyte-scale infrastructure for data and AI • Drive cross-functional alignment and strategic platform decisions • Set product vision and roadmap for platform areas • Manage engineering team and grow organization