{"id":588387,"url":"https://alion.io/job/opentable-staff-machine-learning-engineer","title":"Staff Machine Learning Engineer","company":{"id":450225,"name":"OpenTable","domain":"opentable.com","url":"https://alion.io/company/opentable-5","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":90,"open_postings":15,"ghost_share":0,"stale_share":0.4,"repost_share":0,"time_to_fill_p50_days":43,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":91000,"max_usd":188000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":23},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CatBoost","optional":false},{"name":"CI/CD","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"Grafana","optional":false},{"name":"Gunicorn","optional":false},{"name":"Helm","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Maven","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Poetry","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Slack","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"TeamCity","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"TGI","optional":false},{"name":"Uvicorn","optional":false},{"name":"vLLM","optional":false},{"name":"XGBoost","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-09-10T07:13:11Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-10-28T20:12:39Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":48},"description":"This hybrid role requires working in the office two days per week.\nWith millions of diners, 70,000+ restaurant partners and 25+ years of experience, OpenTable, part of Booking Holdings, Inc. (NASDAQ: BKNG), is an industry leader with a passion for helping restaurants thrive. Our world-class technology empowers restaurants to focus on what matters most - their team, their guests, and their bottom line - while enabling diners to discover and book the perfect restaurant for every occasion. \nEvery employee at OpenTable has a tangible impact on what we do and how we do it. You’ll also be part of a global team and its portfolio of metasearch brands. Hospitality is all about taking care of others, and it defines our culture.\nAbout the Role\nThe Data Science team at OpenTable supports a wide range of initiatives targeting diners, restaurants, and internal stakeholders. The team is expanding its capabilities across multiple areas, including building AI Agents to power restaurant search and discovery as well as AI-augmented products for restaurant management.\nAs a Staff Machine Learning Engineer, you will set the technical direction for how OpenTable builds, serves, and operates machine learning systems in production. You will partner with Machine Learning Scientists and engineers across the company to take models from experimentation to reliable, monitored production services --- and you will define the standards and patterns the rest of the team builds on.\nThis is a deliberately engineering-forward role. We are looking for an engineer who builds and operates production systems, rather than a modeller who deploys occasionally. The strongest candidates will bring hard-won judgment from more than one organization about how mature ML teams actually work, and the ability to apply it here. This posting is for an existing vacancy.\nKey Initiatives\nAI Agents for restaurant discovery\nPersonalized recommendations for diners\nDeveloping and serving high-throughput predictive models for strategic marketplace optimization initiatives\nBuilding and integrating tools into our agentic platform via LLM tool calls, MCP, and Agent-to-Agent protocols\nMultimodal understanding of restaurant content (text, images, geospatial)\nCreating an AI-powered platform for restaurant partners to gain insights into their business performance and diner demand\nWhat You'll Own\nTechnical direction for how models are served, deployed, and monitored; the architecture, the patterns, and the tradeoffs behind them.\nProduction ML services that are high-throughput, low-latency, and observable, from design through operation.\nEngineering standards for ML systems: testing, CI/CD, observability, alerting, rollback, and on-call practice.\nAmbiguous, cross-team problems: scoping them with Product Managers and stakeholders, then ruthlessly prioritizing what the team actually builds.\nRequirements\n7+ years of professional software engineering experience, with a substantial portion spent building and operating machine learning systems in production.\nBreadth of industry perspective. You have seen how ML systems are built and operated at more than one organization, and can speak to industry-standard practices, common reference architectures, and where the real tradeoffs lie.\nHands-on experience with a major cloud platform (AWS, GCP, or Azure) as a primary model-serving environment, including its managed services for deployment, scaling, and observability.\nDeep engineering fundamentals: distributed systems, service and API design, concurrency, latency and throughput tradeoffs, testing discipline, and genuine production ownership including on-call.\nStrong command of Python and proficiency in at least one strongly typed language (Java preferred).\nDemonstrated experience training, serving, and deploying ML models at production scale.\nProduction MLOps ownership: model and feature monitoring, drift and data-quality detection, retraining and promotion workflows, versioning, safe rollout and rollback, and incident response when a model misbehaves.\nA track record of technical leadership: leading multi-quarter projects, influencing engineering decisions beyond your immediate team, and coordinating with Product Managers and other stakeholders.\nStrong Preference\nServing LLMs in production: inference infrastructure, GPU utilization, batching and caching strategies, quantization, and managing the latency/cost frontier (vLLM, TGI, TensorRT-LLM, or similar).\nApplied ML depth in ranking, recommendations, classification, NLP, RAG, and/or agentic systems.\nKubernetes in production at meaningful scale.\nExperience developing ETL jobs (especially Spark) or data warehouse infrastructure.\nFamiliarity with A/B testing design and analysis best practices.\nExperience introducing new tooling or platform capability to a team and driving adoption.\nTech Stack\nWe do not expect experience with everything on this list; it is here so you know what you would be working with.\nPipelines: Spark, Airflow, EMR, SageMaker, Snowflake, S3, Delta Lake\nML: PyTorch, XGBoost / CatBoost, LLMs, LangChain, LangSmith\nDeployment: Docker, Kubernetes, Helm, Prometheus, Graphite / Grafana\nInfrastructure: Kafka, ElasticSearch, Postgres, MongoDB, Redis, Qdrant\nBuild: Poetry, FastAPI, Flask, Gunicorn / Uvicorn, Spring, Maven, TeamCity\nWhy This Team\nThe ML team at OpenTable has two opposing challenges which manifest themselves as opportunities:\nOpenTable is the world's leading provider of online restaurant reservations, seating more than 25 million diners per month via online bookings across approximately 70,000 restaurants. It has a massive wealth of diner and restaurant data going back over 25 years.\nOpenTable fields a lean team, with just over 1,600 employees globally. The ML team is currently fourteen people, but striving to grow.\nAs a member of the team, you will benefit from these factors because your projects will have sufficient data and usage to be interesting and have a meaningful impact, and you will have the opportunity to work on a variety of interesting projects across the company. However, you will be forced to think critically and ruthlessly prioritize, since the team has finite bandwidth. If this sounds like an interesting challenge, we look forward to hearing from you.\nBenefits and Perks\nWork from (almost) anywhere for up to 20 days per year\nFocus on mental health and well-being:Company-paid therapy sessions through SpringHealth\nCompany-paid subscription to Headspace\nAnnual company-wide week off a year - the whole team fully recharges (and returns without a pile-up of work!)\n\nPaid parental leave\nGenerous paid vacation + time off for your birthday\nPaid volunteer time\nFocus on your career growth:Development Dollars\nLeadership development\nAccess to thousands of on-demand e-learnings\n\nTravel Discounts\nEmployee Resource Groups\n20 days of paid time off\nPrivate health and dental insurance\nLife and Disability insurance\nThe best connections happen face-to-face, whether you’re sitting down to dinner or having coffee with a coworker. That’s why OpenTable has adopted a hybrid workplace model. This role aligns with that approach, with an expectation of coming into the office two days a week-giving employees the best of both worlds: in-person collaboration and flexibility.\nThere are a variety of factors that go into determining a compensation range, including but not limited to external market benchmark data, geographic location, and years of experience sought/required.\nWe offer a competitive base salary and benefits including: health benefits; flexible spending account; retirement benefits; life insurance; paid time off (including PTO, paid sick leave, medical leave, bereavement leave, floating holidays and paid holidays); and parental leave benefits. This role is eligible to be considered for an annual bonus and equity grant.\nWork Environment & Flexibility\nAt OpenTable, we pride ourselves on fostering a global and dynamic work environment. As a team member with us, you will benefit from a schedule tailored to accommodate a global workforce operating across multiple time zones. While the majority of your responsibilities may align with conventional business hours, there will be instances where you are expected to manage communications - via calls, Slack messages, or emails - outside of regular working hours to effectively collaborate with international colleagues, respond to restaurant partners, and/or address urgent matters. OpenTable will always abide by and consider local laws and regulations.\nInclusion\nWe’re committed to creating a workplace where everyone feels they belong and can thrive. We know the best ideas come when we bring different voices to the table, so we're building a team as dynamic as the diners and restaurants we serve-and fostering a culture where everyone feels welcome to be themselves.\nIf you need accommodations during the application or interview process, or on the job, we’re here to support you. 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