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San Francisco, California, United States
Onsite
Watershed is the enterprise sustainability platform. Companies like Airbnb, Carlyle Group, FedEx, Visa, and Dr. Martens use Watershed to manage climate and ESG data, produce audit-ready metrics for voluntary and regulatory reporting including CSRD, and drive real decarbonization. We are looking for team members who love product-building, want to work hard at a mission-oriented startup, and will collaborate with us in shaping the culture of a growing team.
We have offices in San Francisco, New York, Denver, London, Paris, Berlin, Sydney, Mexico City, and remote team members across the US and Europe. We hope that you'll be interested in joining us!
We're hiring a builder to architect and operate the systems, applied AI, and data infrastructure Watershed's GTM organization runs on. This is both a systems engineering role and an applied AI role - and the work is shifting how modern GTM systems get built. You'll build the systems and AI that let our GTM teams spend more of their time on customer outcomes.
You'll be on the team that architects and operates the technology the GTM organization runs on: CRM, call intelligence, data enrichment, outbound activation, and the agent orchestration layer that wraps them. You'll partner with data science on the GTM data-to-action loop - designing what we capture, how we translate signals into insight, and how insight becomes a workflow the GTM organization adopts. Your work accelerates and extends our existing push toward AI-native GTM operations.
This is a role for someone who has already rewired their day around AI and pushed others around them to do the same. You'll have real scope to define how GTM Engineering shows up at Watershed: the systems we build, the agents we run, and the patterns we set for how the wider GTM organization adopts AI.
Success means a GTM organization where leadership trusts the systems, the team adopts what we ship, and AI is embedded in how people work day-to-day. This work is measured by adoption and business impact: pipeline velocity, sales efficiency, and customer outcomes.
This role is based in our San Francisco or New York office.
You will:
Drive GTM systems end-to-end - Salesforce (data model, integrations, automation), CPQ, lead routing, call intelligence, data enrichment, outbound activation, and scaled support - designing, developing, and shipping what we build, or leading the evaluation and implementation of what we bring on. Partner closely with Marketing Operations, Sales Operations, Deal Operations, CS Operations where the work intersects their scope.
Build the applied AI layer of the GTM stack: agents and workflows the GTM organization relies on day-to-day, driving pipeline velocity, sales efficiency, and customer outcomes.
Build the outbound signal engine with us: signal ingestion, enrichment, play orchestration, and attribution - from the data model to the SDR-facing notification layer - shipping the pilot plays that prove it generates pipeline.
Build out the GTM Engineering operating infrastructure: deployment pipeline, branching and sandbox management, testing standards, backups, and the AI dev environment (Claude Code, agents, skills) the team ships in.
Partner with data science on the GTM data-to-action loop - driving how we capture data, and how signal becomes insight and insight becomes workflow that moves business outcomes.
Bring technical capabilities to the GTM Operations and Marketing Ops teams to accelerate our org's velocity on what we can ship across our systems, our data, and AI tooling.
Drive adoption and measurement like a product owner: discover user pain, ship, change-manage, measure trust and business impact across the AI portfolio, and sunset what doesn't earn its place.
Build the technical foundation that lets the wider GTM organization work with AI - tools, connections, documentation, supporting agent builds, and operating standards. Partner with Enablement on user-facing training and AI fluency programs.
The ideal candidate brings:
5+ years in a customer-facing or GTM-facing technical role at high-growth B2B SaaS or AI companies, including Revenue Systems, GTM Engineer, or RevOps
AI is embedded in the current workflow - deep experience with coding tools (like Cursor or Claude Code), and enterprise agent tooling
Broad experience managing the modern GTM tech stack: data enrichment, lead routing, sales engagement, call intelligence, chat, CPQ, support (experience with Salesforce, Gong, Clay, and/or LeanData preferred)
Deep technical background in system design and architecture - including data model, integrations, automation, and the tradeoffs between declarative and code-based solutions
Experience managing development environments and deployment pipeline (such as Gearset)
Must be willing to work from an office 4 days per week (except for remote roles)
Watershed has hub offices in San Francisco, New York, London, and Mexico City and satellite offices in Denver, Sydney, Paris, and Berlin. Where we have offices, employees are expected to be in office for 4 days per week. Certain jobs are open to being remote and will be specifically noted on the jobs page and in the job description if so.
What’s the interview process like?
It starts the same for every candidate: getting to know the team members through 1 to 2 conversations about Watershed, your experience, and your interests. Next steps can vary by role, but usual next steps are a skill or experience interview (e.g. a coding interview for an engineer, a portfolio review for a designer, deeper experience call for other roles) which leads to a virtual or in person interview panel. We prioritize transparency and lack of surprise throughout the process.
What if I need accommodations for my interview?
At Watershed, we are dedicated to ensuring an inclusive recruitment process. We provide reasonable accommodations for candidates with disabilities, long-term conditions, mental health needs, religious observances, neurodivergence, or pregnancy-related support requirements. If you need assistance during your process, please contact your recruiter.
