Builds benchmarks and runs experiments in document AI, then publishes high-velocity technical content like blogs and analyses to drive developer awareness and adoption. Requires strong Python/ML engineering and rapid technical writing skills.
140k – 220k/yr
HybridML Engineering
About the role
Responsibilities
Design, build, and maintain comprehensive benchmarks for document parsing and understanding
Publish high-quality technical content at a weekly cadence (blog posts, benchmark reports, technical comparisons, tutorials)
Stay deeply current with the document AI landscape - new models, papers, competitors, techniques
Run experiments and translate findings into publishable artifacts quickly
Produce technical analyses that demonstrate our capabilities against alternatives
Contribute to open-source examples, notebooks, and documentation
Collaborate with the core ML team to surface improvements and capabilities worth highlighting
Engage authentically with the developer community through technical content
Required Qualifications
Experience in software engineering (ML engineering + research a bonus)
Strong software engineering fundamentals with production Python experience
Understanding of modern ML techniques, particularly in computer vision, NLP, or multimodal learning
Demonstrated ability to write clearly, quickly, and authentically about technical topics
Bias toward shipping - comfortable publishing at blog pace, not paper pace
Ability to read, understand, and synthesize research papers rapidly
Scrappy and self-directed - can identify what's worth writing about and execute end-to-end
Track record of high-velocity output in fast-paced environments
Preferred Qualifications
Experience with vision-language models, transformer architectures, or document AI specifically
Existing portfolio of technical writing (blog posts, tutorials, technical documentation)
Experience building evaluation frameworks or benchmarks
Familiarity with OCR, layout analysis, table extraction, or document structure understanding
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