Technical Writer - AI Science

About Alembic
Alembic is where top engineers are solving marketing's hardest problem: proving what actually works. If you're looking for frontier technical challenges at an applied science company, this is the place.
At Alembic, we're not just building software—we're decoding the chaos of modern marketing. Join Alembic to build trusted systems that Fortune 100 companies use to make multimillion-dollar decisions. We're backed by leading tech luminaries including WndrCo (founded by DreamWorks founder Jeffrey Katzenberg), Jensen Huang, Joe Montana, and many more.
About The Role
We're looking for a Technical Writer focused on R&D and AI Science to document and communicate our AI/ML research, algorithms, and data products. You'll translate complex technical concepts into clear, accurate documentation that enables knowledge sharing across our R&D team and accelerates innovation.
This is a unique opportunity to work at the intersection of technical writing and cutting-edge AI/ML research—documenting breakthrough algorithms, Python SDKs, and the science behind marketing causation.
What You'll Do

Create comprehensive documentation for internal AI/ML systems, algorithms, and research methodologies within first 3 months
Document Python SDKs, APIs, and data tools to accelerate developer onboarding and reduce support requests by 40%
Translate complex research findings into technical specifications, white papers, and internal knowledge base articles
Build and maintain a scalable documentation system that grows with R&D team expansion
Collaborate with data scientists and engineers to capture and disseminate best practices and technical insights
Produce technical blog posts and thought leadership content that showcases Alembic's scientific innovations
What Will Help You Succeed
Technical Writing & Communication

3-5+ years technical writing experience with focus on software, data, or AI/ML products
Strong technical aptitude - ability to quickly learn and explain complex algorithms, data structures, and AI/ML concepts
Excellent communication skills - can translate complex technical concepts for different technical audiences
Experience documenting APIs and SDKs using modern documentation tools (Swagger/OpenAPI, Sphinx, MkDocs)
Experience with documentation-as-code workflows (Git, Markdown, version control)
Technical Skills

Python proficiency - comfortable reading Python code and documenting APIs, libraries, and data pipelines
Understanding of data science workflows - familiarity with concepts like ETL/ELT, data modeling, ML pipelines
Self-directed learner - comfortable diving into codebases, asking questions, and figuring things out independently
Advanced Experience

Background in computer science, data science, or related technical field
Experience working embedded with engineering or research teams
Familiarity with ML/AI concepts (model training, inference, feature engineering, causation vs correlation)
Experience creating technical diagrams and visualizations (system architecture, data flows)
Understanding of statistical concepts and causal inference methodologies
Experience with Jupyter notebooks and literate programming approaches
Prior work documenting data platforms, analytics tools, or ML infrastructure
Nice to Haves

Contributions to open-source technical documentation
Experience writing research papers, technical blog posts, or white papers
Background in mathematics, statistics, or quantitative fields
Familiarity with GPU computing or high-performance computing documentation
Experience with tools like Notion, Confluence, ReadTheDocs, or Docusaurus
Interest in marketing analytics, causal inference, or econometrics
Why You Might Be Excited About Alembic

Hard problems with real impact: You'll tackle the hardest challenges in marketing analytics while building systems that influence multimillion-dollar decisions at Fortune 100 companies
Technical autonomy: You want ownership over technical decisions and the freedom to solve complex problems your way
Cutting-edge technology: Work with advanced AI/ML algorithms, composite AI solutions, private NVIDIA DGX clusters, and the latest in data processing at scale
Elite team: Join top engineers who thrive on challenging problems and high-impact work
Startup upside: Early-stage equity opportunity with experienced leadership and proven product-market fit
Why You Might Not Be Excited

If you only want to tell people what to build instead of building and coding alongside them, we're not the environment for you
You prefer company practices with 100% built-out process for every detail
You prefer static over dynamic. Projects, priorities, and roles will adapt to your skill set and goals. Though we have real paying customers and a playbook for growth, we proudly remain an early-stage startup
Compensation Range: $162K - $192K