Lexsi Labs is one of the leading frontier labs focusing on building aligned, interpretable and safe Superintelligence. Most of the work involves developing new methodologies for efficient alignment, interpretability, lead-strategies, and foundational tabular model research. Our mission is to make AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety.Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At Lexsi.ai, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance.As a
Technical Content Lead
at Lexsi.ai, you will be uniquely positioned to shape how the world understands and adopts our platform. You will operate at the intersection of research, product, and engineering, translating complex systems into clear, technically rigorous narratives. You’ll help build trust with developers, data scientists, ML engineers, and platform teams across startups, mid-market, and enterprise.This is a high-ownership role where
startup speed meets technical depth
, with outcomes measured in
credibility, clarity, and adoption
.
Responsibilities
- Own Lexsi.ai’s technical content strategy and execution across audiences: AI engineers, data scientists, AI Researchers, SDEs, platform teams, and enterprise stakeholders.
- Produce high-signal technical content that engineers actually use: deep-dive blog posts, technical explainers, and benchmarks; tutorials, notebooks, guides, and reference implementations; architecture breakdowns, product narratives, and “how it works” content.
- Convert internal research and product work into external-facing assets: interpretability methods, alignment approaches, evaluation methodology; ML observability, debugging workflows, auditability and governance story
- Create and maintain technical and product documentation in collaboration with engineering: onboarding, SDK/API docs, integration guides, and troubleshooting playbooks
- Partner with Product, Research, MLEs, and SDEs to: Identify adoption friction points; improve activation and “time-to-first-value” through content-driven UX clarity; ensure technical claims are precise, testable, and defensible
- Build a repeatable content engine that supports user acquisition in the US and Europe:developer-first distribution (GitHub, communities, conferences); launch narratives, release notes, changelogs, and product updates
- Support external visibility: co-author technical briefs/whitepapers; help prepare conference submissions, talks, demos, and workshops; publish work in open forums where appropriate (blogs, open repos, arXiv-style notes)
Qualifications
- Strong technical foundation in one or more of: ML / DL, MLOps, data systems, applied AI, or platform engineering
- Demonstrated ability to write and ship technical content that developers trust: blogs, docs, tutorials, notebooks, GitHub READMEs, research notes, or similar
- Comfortable engaging directly with engineers and researchers to extract insights and turn them into publishable assets.
- Hands-on familiarity with modern ML tooling and workflows (examples: PyTorch/TensorFlow, pipelines, eval stacks, model debugging, observability).
- Able to communicate complex ideas clearly without diluting technical correctness.
- High ownership mindset: can run end-to-end from idea → draft → code/examples → review → publish → distribution → iteration.
Nice to Have
- Experience writing content related to interpretability, alignment, evaluation, model monitoring, or ML governance.
- Ability to create strong diagrams and technical visuals (system diagrams, evaluation flows, architecture maps).
- Prior open-source contributions or experience maintaining developer-facing tooling
- Experience collaborating with growth/product on activation and funnel improvements (PLG motion).
- Experience speaking at meetups/conferences or running workshops.
What Success Looks Like (First 6–12 Months)
- Lexsi.ai content becomes a trusted reference in engineer communities (shared in Slack groups, GitHub issues, forums).
- Clear improvement in activation metrics driven by docs/tutorials (faster time-to-first-value).
- A consistent, recognizable technical voice across docs, launches, and thought leadership.
- Multiple “flagship” pieces of content that reliably pull qualified developers into trying the product.
How to Apply
Please Send
- 2–3 examples of technical writing you’ve published (or private samples), and
- GitHub/code samples if relevant, plus
- a short note on what kinds of technical content you love creating (and for whom).
