Responsibilities

• Contribute to the global developer relations strategy. Help define how Nscale engages developers across content, community, events, and education to drive platform adoption.
• Drive developer adoption. Create self-serve content that supports every stage of the developer journey, from discovery and evaluation to building and deploying AI workloads on Nscale.
• Create a developer content engine. Oversee the creation of high-quality technical resources, including documentation improvements, code samples, demos, tutorials, notebooks, blog posts, and videos.
• Represent Nscale in the developer ecosystem. Speak at developer conferences, meetups, and hackathons; build strong relationships with open-source communities, AI-native companies, and the broader AI research community.
• Act as the bridge between developers and internal teams. Serve as a key connection point across Product, AI Platform Engineering, and Marketing. Identify friction, gaps, and emerging use cases from the developer ecosystem, feeding these insights back into product and platform teams while staying closely aligned with roadmap direction to proactively shape external content, demos, and ecosystem contributions.
• Build and scale developer programs. Launch initiatives such as ambassador programs, open-source initiatives, research collaborations, and training programs that help developers succeed on Nscale.
• Instrument developer growth. Support product-led developer growth by partnering with growth teams to define KPIs and dashboards that track self-serve adoption, usage, content engagement, and community growth.
• Experiment and evolve. Test new formats for developer education (live coding, interactive labs, tutorials, community challenges) and scale what drives adoption.

Requirements

• 8+ years of experience in developer relations, advocacy, technical marketing, or developer-focused product roles within technology companies, with a track record of building programs that drive measurable product adoption.
• Bachelor's Degree in Machine Learning, Engineering, Computer Science, Technical or other related discipline or equivalent experience
• Strong technical background in ML and passionate about cloud infrastructure, with hands-on experience running AI workloads at scale (e.g., GPU compute, distributed systems, Kubernetes, model fine-tuning, and production inference), and familiarity with modern AI tooling including open-source frameworks, libraries, and agent-based systems.
• Deep understanding of how both AI-native teams and enterprises adopt AI infrastructure, with the ability to position beyond demos by showing how workloads evolve from prototype to production across development, testing, and production environments.
• Strong point of view on the evolving AI landscape, with the ability to identify emerging models, techniques, and developer patterns early and translate them into high-impact content, demos, and strategic opportunities.
• Deeply connected within the AI/ML ecosystem, with strong industry ties across leading companies, open-source communities, or research groups (e.g., NVIDIA, Hugging Face, PyTorch, TensorFlow, JAX, Kaggle, Modular, GPU Mode, Python Foundation).
• Proven ability to communicate complex technical concepts and engage developer communities across content, speaking at events, open source, and online platforms
• Data-driven mindset, with experience measuring developer adoption and engagement metrics; familiarity with product-led growth (PLG) motions is a plus.
• Comfortable collaborating with engineering, product, and marketing teams, and communicating insights and impact to senior leadership.