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Watershed

Staff software engineer, AI product

London, England, United Kingdom

Location
London, England, United Kingdom
Work
Hybrid
Employment
Full-time
Level
Lead / staff
Function
Engineering
Pay
Not stated
Posted
14 months ago

About the role

About Watershed

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!

The role

We're looking for a software engineer specializing in AI to join our sustainability AI reporting team. You will be a technical leader in helping us build a world class AI tool for companies to manage and report on their progress in sustainability. You'll leverage LLMs, embeddings, and other AI technologies to deliver features to our customers, and help lay the technical foundations for AI at Watershed.

We are looking for this hire to be based in our HQ in London 4 days per week.

In this role you will

  • Prototype, develop and iterate on a new product area for Watershed, giving our customers powerful AI tools for information extraction and report generation
  • Productionize and launch core technologies to power AI features on top of a wealth of operational sustainability data
  • Collaborate with product and other AI engineering teams to set product and technical strategy
  • Keep up with developments and state-of-the-art in AI to determine what is relevant to Watershed
  • Write performant, well-crafted, tested, and maintainable code across our technical stack

You might be a good fit if you have

  • We’re looking for 2+ years of working building AI powered products and 5+ years in software engineering.
  • Experience building products using LLMs, embeddings and other ML technologies
  • Full lifecycle experience of building, deploying and monitoring products that leverage LLMs, embeddings or other ML technologies
  • Experience leading cross functional teams in an innovative fast moving environment
  • A strong foundational understanding of machine learning models and practice, including model evaluation and performance
  • Strong full-stack development skills

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.

Source

Posted by Watershed on their own hiring page, jobs.ashbyhq.com. OptiTalent shows it as published, read 07 Nov 2026 04:20 UTC; the original is the reference.

View the original ad ↗

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