Platform Engineer; Forward Deployment

Who We Are

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end‑to‑end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.

Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer‑first software with cost‑efficient, large‑scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.

We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and First minute.

We are seeking experienced Platform Engineers to partner directly with customers to architect, build, and deploy production integrations. In this role, you will work across AI workflow implementation, full‑stack feature development, security, and billing integration, to ensure your partner’s success. You will own communication of customer requirements, scoping of bespoke problems into generalizable solutions, and expanding the Lightning Platform to fulfill your customer’s needs.

This role sits at the intersection of software engineering, research engineering, AI infrastructure, product thinking, and customer engagement. You’ll work within Lightning’s internal product and engineering organizations to deliver production‑ready AI systems that help customers realize value quickly and scale with confidence.

This is a hands‑on engineering role that combines software development, AI infrastructure, technical customer engagement, and product thinking. Successful candidates will be highly technical, customer‑oriented builders who thrive in fast‑moving environments and enjoy solving ambiguous, real‑world AI systems problems.

This role is based in one of our hubs (New York City, San Francisco, Seattle, or London), with a minimum of 2 in‑office days per week and occasional team and company offsites.

• Partner directly with customers to design, implement, and deploy end‑to‑end AI systems and workflows on Lightning’s platform
• Translate vague customer objectives into clear technical specifications, proof‑of‑concepts, and scalable production implementations
• Own customer technical engagements end‑to‑end, from early discovery and architecture through deployment, monitoring, and expansion
• Develop and maintain production‑grade software systems and services using modern programming languages, with a strong preference for Python
• Build reliable, observable systems with strong attention to latency, throughput, quality, scalability, and cost efficiency in production environments
• Debug and optimize AI systems across inference infrastructure, model behavior, APIs, and distributed workloads to improve performance and reliability
• Work closely with customer engineering teams throughout the full lifecycle of AI deployments, including technical discovery, implementation, deployment, and scaling
• Collaborate cross‑functionally with Lightning’s product and engineering teams to improve platform capabilities, influence roadmap priorities, and identify opportunities for reusable product improvements
• Navigate ambiguity with sound technical judgment, making thoughtful tradeoffs and selecting the right tools and approaches without introducing unnecessary complexity
• Demonstrate strong ownership and accountability in execution, with a commitment to delivering high‑quality outcomes for both customers and internal teams

Required Qualifications

• Strong software engineering experience building production full‑stack applications, including modern frontend frameworks such as React and Type Script, and backend services in Python, Go, or similar programming languages.
• Experience working directly with customers in highly technical environments, such as Forward Deployed Engineering, Solutions Engineering, Applied AI Engineering, Technical Product Engineering, or related roles
• Familiarity with AI/ML pipelines and the lifecycle of model development, evaluation,…