Compensation Specialist

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 Firstminute.

The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:

• Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.
• Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.
• Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.
• Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.
• Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.
• Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.

At a company scaling this fast, compensation is a modeling problem before it's an HR problem, and we're looking for someone who treats it that way.

This is a build role. You'll own compensation philosophy, incentive and equity design, and the models and tools the business actually runs on: market research turned into clear recommendations, offer and leveling frameworks that People and Finance reach for, and fast, defensible answers in the moments that matter: performance cycles, offer decisions, retention calls.

If your background comes from strategic finance, FP&A, analytics, or compensation built on real modeling, and you'd rather build the model than hand it off, keep reading.

• →Own the model. Build and maintain the comp and equity models, ranges, and scenario tools the company runs on, including cash-versus-equity tradeoffs. You build these yourself.
• →Own the cycles. Run annual and off-cycle comp planning end to end: strategy, budget, execution. Stay hands-on through the last approval.
• →Turn market data into decisions. Benchmark relentlessly against the AI, infrastructure, and tech companies we compete with for talent, using Radford, Pave, OpenComp, Levels.fyi, or similar. Ship guidance, not raw data.
• →Build tools people use. Develop offer decisioning, leveling guidance, and performance-cycle resources that cross-functional teams rely on to move faster and with more confidence.
• →Support performance cycles. Keep comp inputs into review and calibration accurate, consistent, and tied to strategy.
• →Run it like a data function. Build reporting and dashboards that surface pay equity, offer competitiveness, and retention risk, so decisions get smarter over time.

• 3+ years in compensation, strategic finance, FP&A, analytics, or a comparably quantitative role, with real ownership of comp planning and the models behind it
• Proven ability to drive compensation planning in step with business and People strategy,  come ready to walk through specifics
• Strong modeling and analytical skills: you build and maintain complex comp and equity models and translate the output into clear recommendations for senior leaders
• A track record using market data (Radford, Pave, OpenComp, Levels.fyi, Mercer, or similar) to benchmark and calibrate pay
• Experience partnering with cross-functional stakeholders, with deep knowledge of at least one competitive talent market (AI, infrastructure, tech, or similar)

• Background in AI, cloud/infrastructure, or data centers
• Equity and incentive design experience, including private-versus-public equity frameworks and the modeling that pre-IPO scaling demands
• Experience operating inside a rapid-scaling, pre-IPO environment, and a firsthand sense of how comp breaks and gets rebuilt at each stage