We are looking for a Senior Machine Learning Engineer to join our Revenue ML team at Discord. This role sits at the intersection of Discord's two most strategic revenue pillars , our growing 1P Shop and our newly launched Game Commerce platform. You'll be the founding ML voice for commerce discovery and personalization, building systems from the ground up that power recommendations, social commerce mechanics, and marketing targeting across both first-party and third-party storefronts.
\nYour responsibilities will include:
\nArchitecting and owning the ML foundations for commerce discovery: user, item, and interaction embeddings that power personalized recommendations across shop surfaces (homepage, cart, post-purchase, wishlist, and more).
\nDesigning and deploying scalable real-time recommendation and ranking systems that support a growing catalog of 1P and 3P items across heterogeneous game publisher inventories.
\nBuilding ML-powered marketing targeting systems that identify the right users for the right campaigns , new buyer discounts, drop campaigns, weekly deals, and seasonal promotions , driving conversion without conditioning users to wait for discounts.
\nLeveraging Discord's unique social graph to build social commerce ML: gifting recipient prediction, group buying conversion modeling, and friend-group recommendations that differentiate Discord from traditional game storefronts.
\nDriving deep learning A/B testing infrastructure and model monitoring to translate experimentation results into actionable product decisions.
\nPartnering closely with Shop, Game Commerce, Revenue Infra, ML Infra, and Data Engineering teams to define ML requirements, surface integration points, and influence the commerce roadmap.
\nTo be successful in this role, you will need:
\n4+ years of experience as a Machine Learning Engineer, with a track record of owning and shipping recommendation or personalization systems end-to-end.
\nDeep expertise in applied deep learning , particularly embedding models, two-tower architectures, and retrieval/ranking systems for e-commerce or content recommendation.
\nStrong proficiency in Python and deep learning frameworks (PyTorch preferred).
\nExperience building and operating real-time ML serving infrastructure at scale, including feature stores, model serving, and A/B testing frameworks.
\nDemonstrated ability to work in early-stage, high-ambiguity environments and build ML systems from the ground up, not just improve existing ones.
\nExperience translating ML evaluation metrics and experiment results into product roadmap decisions and business impact.
\nStrong cross-functional instincts , you're comfortable partnering with product, engineering, data science, and business stakeholders to align on priorities and drive execution.
\nBonus skills include experience applying graph ML or social network signals (social affinities, community behavior) to recommendation or personalization problems, familiarity with personalized marketing systems: lifecycle targeting, audience segmentation, and campaign optimization, and familiarity with loyalty, rewards, or incentive programs.
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\nWe are seeking a highly skilled and experienced Staff Software Engineer to join our Communication Products team. As a Staff Engineer, you will be responsible for leading the technical vision for ML-powered messaging features, architecting and delivering intelligent capabilities end-to-end, and partnering deeply with ML and product teams.
\nThe Difference You Will Make:
\nAs a Staff Engineer on the team, you will define and drive the technical strategy for integrating ML capabilities into Airbnb's messaging products, including smart replies, message classification, content moderation, translation, and conversational assistance. You will also own the full lifecycle of ML-powered features: from prototyping and experimentation through launch, monitoring, and iteration.
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\nHow We'll Take Care of You:
\nOur job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
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\nYou will join a team that ships products used by thousands of the world's most sophisticated data and AI organizations. You will drive the vision and roadmap for AI platform product areas and define how customers build, train, deploy, and monitor AI and ML systems on Databricks. You will collaborate across engineering teams to deliver an integrated and powerful path from experimentation to production.
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\nPlaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use.
\nMaking data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively.
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\nFounded by British makeup artist and beauty entrepreneur Charlotte Tilbury MBE in 2013, Charlotte Tilbury Beauty has revolutionised the face of the global beauty industry.
\nThe AI & ML Engineering team accelerates the adoption of AI across the business, championing innovation while ensuring our machine learning products are robust, scalable, and cost-efficient.
\nResponsibilities:
\nPartner with stakeholders to scope problems and identify the right solution - whether leveraging existing AI tools or building custom workflows & solutions.
\nDesign and implement agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP-first stack.
\nBuild and maintain production ML pipelines and services for non-GenAI use cases (e.g. recommender systems, customer segmentation models, marketing optimisation modules, leveraging supervised, unsupervised and/or econometric modelling approaches).
\nDevelop APIs and microservices for AI/ML solutions, ensuring security, scalability, and observability.
\nImplement CI/CD for ML services, writing infrastructure as code, and monitoring for model/data drift and performance.
\nEstablish robust guardrails for safe AI usage, including prompt security, practical evaluation frameworks, and compliance with privacy regulations.
\nDrive and evangelize best practices, reusable templates, and documentation to scale AI/ML delivery across the business.
\nCollaborate with data engineers, data scientists, front & back-end engineers, product managers, legal & infosec colleagues to deliver impactful solutions end-to-end.
\nWho you will work with
\nThe AI & ML Engineer Lead and the wider data team.
\nAbout you
\nThe role requires a blend of technical depth and product sense, including:
\nBachelor’s or Master’s degree in Computer Science, Engineering, or related field.
\nStrong Python engineering skills (FastAPI, testing, typing) and experience with cloud-native development (GCP preferred).
\nHands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks such as LangChain and LangGraph.
\nSolid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring.
\nProven experience deploying and operating ML systems in production (batch and real-time).
\nFamiliarity with RAG architectures, prompt engineering, and evaluation techniques.
\nStrong grasp of security, privacy, and governance principles (IAM, secrets, PII handling).
\nExcellent communication skills and ability to work with non-technical stakeholders.
\nIn addition to the above, we would LOVE if you have:
\nExperience with vector databases and retrieval strategies.
\nKnowledge of recommender systems and ranking models.
\nFamiliarity with LLM evaluation tools (e.g., RAGAS, TruLens, LangSmith, Arize).
\nExposure to feature stores, data lineage, and observability stacks.
\nExperience in e-commerce or retail environments.
\nDemonstrable ability to weigh up build/build/configure decisions in the LLM space.
\nWhy join us?
\nBe a part of this values driven, high growth, magical journey with an ultimate vision to empower everyone, everywhere to be the best version of themselves.
\nWe’re a hybrid model with flexibility, allowing you to work how best suits you.
\n25 days holiday (plus bank holidays) with an additional day to celebrate your birthday.
\nInclusive parental leave policy that supports all parents and carers throughout their parenting and caring journey.
\nFinancial security and planning with our pension and life assurance for all.
\nWellness and social benefits including Medicash, Employee Assist Programs and regular social connects with colleagues.
\nBring your furry friend to work with you on our allocated dog friendly days and spaces.
\nAnd not to forget our generous product discount and gifting!
\nAt Charlotte Tilbury Beauty, our mission is to empower everybody in the world to be the most beautiful version of themselves.
\nWe celebrate and support this by encouraging and hiring people with diverse backgrounds, cultures, voices, beliefs, and perspectives into our growing global workforce.
\nBy doing so, we better serve our communities, customers, employees - and the candidates that take part in our recruitment process.
\nIf you want to learn more about life at Charlotte Tilbury Beauty please follow our LinkedIn page!
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