Technical Content Lead

Responsibilities

• Write technical content. Write original blog posts, social copy, research stories, technical essays, whitepapers and explainers about Normal's hardware and applied AI research. Develop the substance and argument directly with scientists, researchers and engineers, lead pieces through expert review, and work with editorial colleagues on framing, voice and publication. Draft technical documentation that supports research communication and customer evaluation.
• Strengthen shared content. Edit, write and co-author external-facing content with technical experts, shaping arguments, strengthening explanations and supporting expert review. Polish presentations and customer collateral for their intended audiences while maintaining technical accuracy. Contribute original writing and technical judgment to website content, explainer videos and technical design assets with Communications and Marketing colleagues. Work with product marketing to explain Normal's specific advantages using industry insight and verified technical evidence.
• Track the industry. Continuously track industry products, competitive positioning, ecosystem developments and value propositions. Keep the Communications and Marketing team current with weekly reports on industry trends and milestones, and timely updates on developments that affect our work.
• Embed with Engineering. Join regular engineering and silicon team meetings to support their content needs and stay current on their work. Brief Communications and Marketing regularly on verified progress, technical definitions, performance, configurations and roadmap or timeline changes. Flag matters needing the Head of Communications' involvement. Apply that knowledge to original writing and broader team projects, keeping public-facing content current. Distinguish measured results from targets and projections. Keep confidential and unreleased information internal until approved for external use.
• Explain technical performance. Work with Engineering to interpret benchmarks and results, develop useful comparisons and explain the performance of Normal Inference Systems to technical and commercial audiences.
• Build customer materials. Develop the technical substance of evaluation materials, decks, briefs and FAQs with colleagues leading product marketing and customer work. Explain system capabilities, integration requirements and workload economics in ways that help customers evaluate the product.
• Make answers findable. Turn recurring customer questions into useful, searchable technical explanations with colleagues in editorial and product marketing. Connect answers to relevant research and evaluation materials, and keep them current as systems and capabilities change.
• Explain the evidence. Develop diagrams, plots and, where useful, reproducible examples or notebooks with internal experts and external collaborators. Give readers and colleagues a clear basis for understanding and checking technical claims.
• Learn from the field. Join relevant research, product and customer discussions to understand questions and constraints. Use lab sessions, roadshows and account feedback to improve materials and identify stories worth developing.
• Ensure technical accuracy. Resolve open questions with the right experts and keep review moving against deadlines. Maintain consistent company language and product naming, and update published content when new evidence changes the answer.

Requirements

• Technical depth and original writing. A portfolio of substantial technical work you authored and can explain in depth, with evidence of consistently publishing strong work at pace. Deep-tech journalism or equivalent in-house writing and editing experience is especially relevant. Research, engineering and technical marketing backgrounds can also qualify with the same evidence of original writing, editorial judgment and delivery. You can engage in substantive discussions with technical leads and develop original writing from primary sources; the depth and quality of your work matter more than a particular degree or title.
• Domain depth and learning speed. The ability to go deep enough into subjects such as AI inference, computer architecture, semiconductors or computational physics to write credibly and precisely. You can build understanding from primary sources and expert conversations, identify gaps in your knowledge and learn challenging new material quickly.
• Technical reasoning. You can read a research paper, question a benchmark and explain a mechanism in your own words. You know when a conclusion depends on an assumption, what makes a comparison valid and which question requires another measurement.
• Working with evidence. Comfort with benchmark data, plots and technical diagrams, and with code or notebooks when they are part of the underlying work. Experience creating runnable examples or reproducible technical artifacts is useful.
• Audience judgment. You can write for a domain expert and adapt the same substance for a customer or executive with clarity and precision. You think about the visual explanation alongside the prose.
• Cross-functional collaboration. You earn the trust of researchers and engineers and bring your own thinking to projects across the team. You share work early, invite contributions and help colleagues strengthen and amplify their content. You can turn new developments into publishable work quickly, manage expert reviews and meet a regular publishing cadence while protecting technical accuracy.
• Travel readiness. Willingness to travel for team collaboration, lab work, customer evaluations and relevant industry events.