Lead end-to-end deployments of AI for hotel revenue teams: integrate PMS/CRS/RMS, ship ML/optimization models and agentic workflows, validate recommendations with revenue managers, define metrics, drive adoption, and hire/mentor the forward deployed engineering team.
telostravel.ai
Lead Forward Deployed Engineer, Hospitality
Remote (US or Canada) · Hybrid/Remote · Reports to the Chief Operating Officer
About telos
telos is an AI revenue decision platform: a decision-infrastructure layer that sits between systems of record (PMS, CRS, RMS, distribution) and the people making high-yield commercial decisions on pricing, inventory, mix, and merchandising. We amplify the judgment of revenue teams by surfacing more good decisions than any team has time to find on its own. Our airline business is live in production with major carriers, and we are now building the equivalent capability for hospitality. This role delivers it, hotel by hotel and brand by brand.
The Role
Forward deployed engineers at telos ship working AI inside client environments. As Lead Forward Deployed Engineer, you sit at the intersection of hotel revenue management practice and applied AI engineering. You embed with hotel commercial teams, own end-to-end delivery on their real data and real systems and stay until revenue managers trust the output enough to act on it.
The hard part of AI in hospitality is not the model. It is the workflow around it: whether revenue managers trust the system, whether it fits how commercial decisions get made, and whether it holds up under the operational pressure of running a hotel portfolio. You have lived that pressure, from the engineering side, the commercial side, or ideally both, and you know how to build for it.
You start as a senior individual contributor with full ownership of client deployments and grow into building the forward deployed engineering team as the hospitality business scales.
What You'll Do
- Own client deployments end to end: scope the engagement, integrate with the client’s PMS, CRS, RMS, and distribution stack, ship the models, and drive adoption inside the revenue organization.
- Design and ship agentic workflows that revenue managers actually adopt rather than second-guess.
- Translate deep hotel revenue management practice into decision logic, validating that agent recommendations match the judgment of seasoned revenue managers and directors.
- Work on-site and remotely with client product, engineering, and commercial leadership to integrate cleanly with hotel systems of record and distribution.
- Define the methodology and metrics standards each deployment is held to, ensuring rigor in how performance is framed and measured.
- Represent telos credibly with brand-level revenue and commercial leaders, the EVPs and heads of revenue who buy and rely on this work.
- Build, mentor, and lead the forward deployed engineering team as the function scales.
Who You Are
You combine hands-on technical delivery with real commercial domain depth. Strong candidates come from one of two paths, and the best come from both:
- Engineering side: a senior technical leader with production experience building and deploying machine learning, forecasting, optimization, and agentic AI, ideally in client-facing or embedded delivery roles.
- Commercial side: experience as a revenue analyst, revenue manager, or revenue director at a large hotel brand, owning real pricing and inventory decisions across multi-property portfolios.
Required: Hotel Systems Fluency
Demonstrated, hands-on familiarity with the systems hotel revenue teams actually run on, such as:
- Revenue management systems (RMS): IDeaS, Duetto, Revenue Analytics, or equivalent, deployed in production at scale.
- Property management systems (PMS): Oracle OPERA or equivalent.
- Central reservation systems (CRS): Amadeus ACRS, Sabre SynXis, or equivalent.
- BI and rate-shopping tools: Lighthouse, STR, Kalibri, Amadeus BI, or equivalent.
- Full command of the hotel revenue management metric set and the methodological distinctions that matter
Required: Technical and Delivery
- Deep lived experience across hotel system engineering or hotel revenue management roles (not back-office IT), pairing senior leadership with hands-on execution.
- Practical experience with major hotel industry APIs such as Oracle Hospitality Integration Platform (OHIP), OpenTravel Alliance (OTA) specs, and Hospitality Technology Next Generation (HTNG) specs.
- Production experience with SQL, Python and modern ML and optimization tooling; comfort across cloud AI services (AWS, Azure, GCP).
- Track record of shipping AI that fits real commercial workflows and earns analyst trust under operational pressure.
- Strong communicator able to move fluently between revenue teams, engineers, and brand-level executives, with a practitioner’s credibility in the room.
Desired
- Experience with agentic AI, RAG, Model Context Protocol (MCP), and Universal Commerce Protocol (UCP) architectures.
- Depth in distribution and channel strategy: OTA and wholesale contracting, channel mix and drift, direct-booking optimization.
- Experience with customer insights, loyalty, and segmentation analytics.
- A revenue management certification (e.g., CRME) and continuing industry leadership presence.
Equal Opportunity
telos is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by applicable law. telos complies with all applicable pay-transparency requirements; where required by law, the relevant pay range will be provided during the application process.
UP.Labs San Carlos, California, USA Office
655 Skyway Rd., San Carlos, CA, United States, 94070
Similar Jobs
Artificial Intelligence • Fintech • Insurance • Marketing Tech • Software • Analytics
Handle inbound and warm insurance sales leads, consult with customers to match coverages, convert leads to policyholders, complete paid licensing and remote training, work scheduled shifts including weekends, and maintain required work-from-home technology and space.
Top Skills:
Dsl)FiberHigh-Speed Internet (CablePc
Artificial Intelligence • Big Data • Cloud • Machine Learning • Software • Business Intelligence • Data Privacy
The role involves designing cloud infrastructure, managing production Kubernetes clusters, optimizing CI/CD pipelines, enhancing developer experience, and ensuring reliable AI workloads. Candidates should have extensive experience in infrastructure and distributed systems engineering with strong coding skills and cloud expertise.
Top Skills:
AWSAzureDatadogDockerElkGCPGoGrafanaJavaKubernetesPrometheusPythonTerraform
Digital Media • Information Technology • News + Entertainment
Manage and grow a portfolio of local clients by designing and presenting data-informed, multi-platform advertising strategies across linear, streaming, and digital channels. Prospect and maintain client relationships, drive revenue and pipeline, forecast and report, collaborate cross-functionally for campaign execution, use attribution insights to optimize performance, and deliver consultative sales solutions to meet business objectives.
Top Skills:
CRMFreewheel
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
