
Cosine provides an AI software engineer named Genie that autonomously completes engineering tasks, implements features, and submits pull requests so development teams can manage work asynchronously.…

Cosine provides an AI software engineer named Genie that autonomously completes engineering tasks, implements features, and submits pull requests so development teams can manage work asynchronously.…
Product: Genie — an autonomous AI software engineer for branching, coding, testing, and opening PRs; platform includes AutoPM for task breakdown and verification
Integrations: GitHub, GitLab, Bitbucket, Jira, Linear, Slack, Vercel, VS Code, JetBrains, CLI
Founded: 2022
Headquarters: London (company described as London-based and also referenced San Francisco in some materials)
Funding (reported): $2.5M seed (Aug 2024); total funding reported ~ $3.53M
Software engineering productivity / developer automation for production-grade codebases
2022
Data and Analytics
$2.5M
Reported participation from Lakestar and Focal
“Includes early-stage investors such as Uphonest, SOMA Capital, Lakestar, Focal, Warrick Shanly, Eight Capital, Gaingels, John Spindler, UpHonest Capital, and Y Combinator”
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Job title: Founding Developer Evangelist / Solutions Engineer - (Enterprise)
Location: London; full in-office working as default
Start date: ASAP
Reports to: CTO, with dotted line to CRO and CEO
Compensation: £60k–£90k base (potential to go higher depending on experience) plus equity
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Cosine at a glance At Cosine, we’re building autonomous AI engineers that plan, write, and ship code inside real development workflows.
Cosine is designed for on-premise and virtual private cloud (VPC) deployments, including fully air-gapped environments. We build our agent tooling entirely in-house and post-train open-source models to deliver reliable, enterprise-grade coding performance in security-critical settings.
In 2024, Cosine achieved a 72% score on OpenAI’s SWE-Lancer benchmark, placing us among the strongest real-world software-engineering AI systems evaluated.
YC-backed and well-funded, Cosine was founded by experienced operators focused on building dependable, production-grade AI.
This role is based in our Hoxton office, five days a week, because close collaboration, fast feedback, and shared context matter for the problems we’re solving. ___________________________________________________________________________
The role At Cosine, enterprise adoption depends on technical trust . Autonomous agents are still poorly understood, often oversold, and rarely inspected with the rigour serious engineering teams require.
We are hiring a Founding Developer Evangelist to make Cosine’s technology legible, credible, and evaluable to the world’s most sophisticated engineering teams.
This Role Sits At The Intersection Of
Content creation is a core tool, but not the end goal.
The goal is to help enterprises understand, trust, and safely evaluate Cosine .
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What You’ll Do
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How Success Will Be Measured (first 6–12 Months)
You’ll Thrive Here If You
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Cosine is an equal opportunity employer. We value diverse backgrounds, perspectives, and ways of thinking, and we’re committed to creating an inclusive and respectful workplace.
We encourage applications from anyone who meets the role requirements, even if you don’t meet every single qualification. If you need reasonable adjustments at any stage of the hiring process, we’re happy to discuss them.
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Compensation, Benefits & Ways Of Working We’re an in-office team, five days a week, by design. We believe the work we’re doing benefits from being together, collaborating closely, and building shared context.
What You Can Expect
We care about focus, sustainability, and doing great work — not performative overwork. We value people who show up, contribute thoughtfully, collaborate well with their colleagues, and then go home.
This role won’t suit everyone. But if you want structure, clarity, strong collaboration, and a team that takes both the work and work-life balance seriously, it’s a great place to be.
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Support enterprise evaluations and pilots (core responsibility)
Act as a technical counterpart to enterprise engineering teams during evaluations, pilots, and early deployments
Join architecture reviews, technical deep dives, and security-focused conversations
Explain how Cosine works in practice: its agent architecture, training approach, evaluation methods, and operational constraints
Help teams understand where Cosine excels, where it currently has limits, and how to evaluate it responsibly
Identify repeated technical objections, points of confusion, and failure modes encountered during real-world evaluations
Create best-in-class technical content (informed by reality)
Produce long-form technical explainers grounded in real evaluation learnings:
Agent architecture and control
Evaluation methods (correctness, reliability, long-horizon behaviour)
Training approaches and research insights
Security, auditability, and on-prem deployment
Translate research-level concepts into content that CTOs, Staff+ engineers, and technical evaluators can reason about
Create clear diagrams, walkthroughs, and examples that help engineers form accurate mental models
Produce high-quality video content where it meaningfully increases clarity or trust
Own Cosine’s technical narrative (enterprise-first)
Develop a coherent, technically rigorous narrative for how Cosine differs from:
“AI wrappers”
Code assistants
Narrow benchmark-driven tools
Ensure Cosine is understood as:
A research-driven ML lab
A production-grade autonomous agent platform
Maintain a consistent voice that emphasises credibility, honesty, and depth, especially for regulated enterprises
Capture and feed back real-world technical insights
Systematically capture insights from enterprise evaluations, including:
Where teams get stuck
What they distrust
Which evals matter most to them
What blocks internal approval
Share structured feedback with product, research, and engineering teams.
Help shape documentation, evals, and product priorities based on repeated patterns observed in the field.
Produce early technical case studies and evaluation write-ups
Create technically rigorous case studies based on pilots and early deployments
Focus on what was tested, how it was evaluated, what worked, and what didn’t