
Job Overview
Location
Toronto / New York
Job Type
Full-time
Category
Operations
Date Posted
April 11, 2026
Full Job Description
đź“‹ Description
- • As the Revenue Operations Manager at Adaptive ML, you will be the operational backbone of our go-to-market organization, owning the systems, data, processes, and analytics that enable Sales, Marketing, and Customer Success teams to operate efficiently and make better decisions faster.
- • You will own and administer the CRM (HubSpot), ensuring data integrity and pipeline hygiene; design and maintain dashboards and reports for sales leadership; build and refine forecasting processes; define and document end-to-end sales processes; build AI-powered automations and internal tooling to streamline workflows; manage territory and account segmentation; administer variable compensation plans; evaluate and manage the sales tech stack; partner with Marketing on lead management and attribution; support GTM planning cycles; identify bottlenecks in the revenue cycle; and serve as the single source of truth for GTM data and metrics.
- • Adaptive ML is a frontier AI startup building a Reinforcement Learning Operations (RLOps) platform that enables enterprises to specialize large language models and deploy them reliably into production workflows with measurable impact. The company raised a $20M seed round led by Index Ventures and ICONIQ in early 2024 and is live with first enterprise customers including Manulife, AT&T, and Deloitte.
- • You will work closely with the Head of Sales and GTM leadership as an analytical and operational counterweight, directly shaping how performance is measured, resources allocated, capacity planned, and the business scaled through the next stage of growth. This role offers the opportunity to build a scalable revenue operations function from the ground up in an early-stage, high-impact environment.
Skills & Technologies
Onsite
About Adaptive ML SAS
Paris-based startup developing a platform that lets enterprises fine-tune and deploy large language models on their own data. The system combines reinforcement learning from human feedback, retrieval-augmented generation and automated evaluation to create specialized, privacy-preserving models that run efficiently on private clouds or on-premise hardware, targeting sectors such as finance, healthcare and legal services.
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