Expedia Group

Developer Relations

• Create high-quality technical content (blog posts, video tutorials, live streams, documentation)
• Build and maintain open-source examples, reference architectures, and starter kits
• Engage with developers, answer technical questions, solicit feedback
• Act as a critical feedback loop to Product and Engineering teams
• Deliver compelling talks and workshops
• Research and experiment with emerging technologies

Developer Relations Engineer III – Developer Experience

Full-stack Engineer

• Define and evolve the end-to-end strategy, metrics framework, and data model for Developer Productivity & Insights, ensuring it aligns with industry best practices
• Design and maintain robust data pipelines, schemas, and dashboards that integrate SDLC signals (Git, CI/CD, Jira, incident/change data, AI tooling) into a coherent, trusted view of engineering effectiveness.
• Lead deep analytical investigations into developer productivity, flow, and quality, turning noisy telemetry into clear narratives, causal hypotheses, and concrete recommendations for senior engineering leadership.
• Partner with DevEx, Platform, and product teams to experiment with changes to tools, workflows, and guardrails, measuring impact (before/after) and operationalizing practices that demonstrably improve outcomes.
• Define, validate, and iterate productivity “north star” and leading indicators (e.g., repo readiness, power-user profiles, AI usage depth) and prune or simplify metrics that don’t drive decisions.
• Act as the technical owner for engineering-wide insights tooling (e.g., LinearB/SEI platforms, internal analytics services), setting standards for data quality, instrumentation, and observability.
• Collaborate closely with the CTO office and senior engineering leaders to shape AI-era productivity strategy, including how AI-assistive and agentic workflows are measured, governed, and reported.
• Mentor engineers and technical leaders on using data to run better teams —from instrumenting services and repos to interpreting metrics, designing experiments, and avoiding misuse of vanity metrics.

Principal Software Development Engineer – Developer Productivity, Insights

Machine Learning Engineer

• Design, develop, and apply machine learning solutions to real-world personalization, product, and business problems, translating ambiguous opportunities into scalable models, experiments, and production-ready capabilities
• Drive end-to-end scientific work across problem formulation, data exploration, feature engineering, model development, evaluation, and iteration, with strong attention to measurable impact
• Partner closely with engineers, product, and business stakeholders to integrate machine learning solutions into services and workflows, including system design, API design, and data modeling considerations where applicable
• Use strong technical judgment to select appropriate methods, validate outcomes, and improve model performance, reliability, and operational quality across multiple problem domains
• Safely integrate and operate AI/ML-enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products
• Contribute deep technical expertise across related domains, helping raise scientific and engineering quality through experimentation, documentation, mentoring, and reusable approaches that support broader team effectiveness

Senior Machine Learning Scientist – Personalization

• Design, build, and evaluate multi-step agentic AI systems, including autonomous agents capable of planning, tool use, memory management, and multi-agent collaboration
• Research and implement state-of-the-art techniques in agentic architectures, such as ReAct, reflection loops, chain-of-thought prompting, and tool-augmented reasoning
• Develop and maintain agent orchestration frameworks, defining how agents decompose tasks, delegate to sub-agents, and handle failure and recovery
• Integrate large language models (LLMs) with external tools, APIs, databases, and code execution environments to enable real-world task completion
• Define and own evaluation frameworks for agentic systems, measuring task success, reliability, latency, cost, and safety across diverse benchmarks and production scenarios
• Collaborate closely with product, engineering, and research teams to translate business requirements into agentic system designs and deliver production-grade solutions
• Identify and mitigate risks specific to agentic systems, including prompt injection, unintended actions, hallucination in long-horizon tasks, and unsafe tool use
• Stay current with the rapidly evolving agentic AI landscape, synthesizing academic research and industry developments to inform the team's technical direction
• Mentor junior ML engineers and scientists, providing technical guidance on agentic design patterns, LLM best practices, and experimentation methodology

Senior Machine Learning Scientist – Agentic Experience

• Design, develop, and apply machine learning solutions to real-world personalization, product, and business problems
• Drive end-to-end scientific work across problem formulation, data exploration, feature engineering, model development, evaluation, and iteration
• Partner closely with engineers, product, and business stakeholders to integrate machine learning solutions
• Use strong technical judgment to select appropriate methods, validate outcomes, and improve model performance
• Safely integrate and operate AI/ML-enabled solutions
• Contribute deep technical expertise across related domains

Senior Machine Learning Scientist, Personalization

• Develop, apply, and advance machine learning solutions for personalization use cases
• Design experiments, evaluate model performance, and use data-driven methods to improve relevance, ranking, recommendation, and overall customer experience
• Partner across engineering, product, analytics, and science teams to define solution approaches, influence technical direction, and deliver ML capabilities
• Contribute technical depth in model development, feature design, data preparation, offline and online evaluation
• Apply strong technical judgment to system design, API design, data modeling, and low-level solution design
• Safely integrate and operate AI/ML-enabled solutions

Machine Learning Scientist III, Personalization

Solutions Engineer

• Lead end-to-end implementation and integration projects for new and existing B2B partners, from discovery and requirements gathering through launch and post-go-live optimization
• Translate partner needs into clear business and technical requirements
• Collaborate with product and engineering teams to design scalable, reusable solutions
• Build strong relationships with external partner stakeholders (technical, commercial, and operational)
• Create and maintain project plans, including timelines, milestones, dependencies, risks, and issues
• Identify and analyze data and performance insights to recommend improvements
• Champion partner feedback into product roadmaps and internal discussions

Partner Solutions Manager – Sydney/Singapore

Data Scientist

• Partner with product teams to clarify business problems, define success metrics, and translate ambiguous questions into structured analytical plans.
• Design, run, and analyze experiments (A/B tests, pre/post, quasi-experimental reads) to measure impact, quantify trade-offs, and guide iteration.
• Perform deep-dive product analysis (funnels, cohorts, segmentation, sizing) to uncover drivers of performance and identify opportunities for growth and customer experience improvements.
• Build reliable datasets and dashboards using SQL and modern BI tools, enabling self-serve product performance monitoring across platforms (web, app, partner).
• Apply solid statistical thinking (probability, sampling, inference, regression) to ensure reads are robust, distinguish signal from noise, and clearly communicate caveats.
• Use Python/R (or similar) to prototype models and advanced analyses when needed (e.g., regression, clustering, simple prediction) and to automate recurring analytics workflows.
• Tell clear, compelling stories that move stakeholders from insight to action—framing context, methods, results, and recommendations for both technical and non-technical audiences.
• Champion data quality and reproducibility by following best practices for data validation, query performance, version control, and documentation.
• Collaborate and upskill others by seeking peer review, sharing best practices, and providing light mentorship to more junior analysts or data scientists.

Data Scientist III, Product Analytics

Engineering Manager

• Lead and grow a frontend engineering team delivering high-quality identity experiences across EG brands
• Own the frontend architecture for Identity web and app surfaces
• Raise the bar on UX and performance by partnering with product and design
• Own problems end-to-end from user experience through client-side architecture, APIs, and integration with backend identity and security services
• Champion engineering excellence
• Develop, coach, and support engineers through clear expectations, regular feedback, and growth opportunities
• Collaborate across disciplines and orgs to align roadmaps and unblock dependencies

Senior Manager, Software Development Engineering

Manager

• Lead day-to-day Global Brand Health Tracking operations
• Support reporting production across recurring deliverables and ad hoc requests
• Partner with Analytics teams to measure impact of marketing campaigns
• Manage external research vendors
• Pilot innovative approaches for measurement programs
• Oversee Tableau dashboard refreshes and data integrity
• Translate raw survey data into clear insights

Consumer Insights Manager

• Collaborate with Software Engineers and ML Engineers/Scientists to design and build large-scale batch and real-time ML systems for advertising use cases
• Propose, lead, and deliver high-impact ML applications across pricing, inventory, content, and trust & safety, aligning technical decisions with business outcomes
• Own the end-to-end lifecycle of mid- to large-scale ML projects, from system design and model development through deployment and production operations
• Establish and promote ML engineering best practices, including model quality, MLOps, observability, and scalable system design
• Mentor junior engineers and support teams in integrating ML into existing production systems
• Partner with senior stakeholders across organizations to drive shared standards, communities of practice, and cross-team learning
• Lead complex, cross-organizational initiatives to improve performance, reliability, and scalability of ML systems

Senior Machine Learning Engineer - Advertising Technology

Director

• Partner with HRBP Leaders to co-create divisional talent agendas
• Assess global talent and learning needs
• Lead succession planning and talent reviews
• Use talent and organizational metrics to identify strengths and risks
• Coach leaders on talent identification and assessment practices
• Influence org design based on best practices and financial guidelines
• Generate insights using talent metrics for informed decisions
• Lead change management for talent products
• Collaborate with various teams for integrated solutions

Senior Director, Talent Strategy

• Design, build, and operate microservices and APIs that support translation orchestration
• Ensure delivery and reliability of globalization services, including implementation, testing, and monitoring
• Develop and maintain SDKs and client libraries in Python, Java, and JavaScript/TypeScript
• Integrate machine translation engines and conduct experiments for AI-powered localization
• Improve translation quality evaluation and collaborate with analytics and localization teams

Globalization Platform Lead

Security Engineer

• Define and evolve security architecture patterns, standards, and reference designs that enable secure-by-design product development across multiple platforms and services.
• Partner with product, engineering, and infrastructure teams to design secure system architectures, including API design, low-level design (LLD), and data models that protect customer and partner data.
• Lead threat modeling, security design reviews, and risk assessments for complex product initiatives, translating security requirements into actionable engineering guidance.
• Provide technical leadership on application security controls (authentication, authorization, encryption, key management, secrets handling) and drive their consistent adoption across domains.
• Guide teams in safely integrating and operating AI/ML‑enabled and AI-driven solutions, tools, or workflows that improve security and product outcomes, including applying AI/ML concepts to real-world products.
• Influence roadmap and technical decision-making by aligning product security architecture with business objectives, regulatory needs, and operational constraints across multiple product areas.

Product Security Architect III

Product Manager

• Define and evolve the product vision, strategy, and roadmap for Autonomous Operations capabilities
• Translate complex operational and technical problems into clear product requirements
• Partner closely with engineering to design, validate, and deliver automation products and services
• Use data-driven insights to prioritize a backlog that increases self-healing, proactive detection, and observability
• Collaborate across multiple domains and platforms to ensure capabilities are reusable and extensible
• Incorporate AI-driven and ML-enabled capabilities into workflows where appropriate