Leigh Marie joined Kleiner Perkins as a partner in 2023 where she focuses on partnering with the next generation of infrastructure & ML application founders. Prior to joining Kleiner Perkins, Leigh Marie began her investing career at Founders Fund. Before that, she was an early engineer and the first product manager at Scale AI, where she built and later led product development for the 3D annotation products, used by many autonomous vehicles, robots, and AR/VR companies as a core step in their machine learning life cycles. Originally from Alabama, Leigh Marie graduated from MIT. Outside of work, Leigh Marie loves to play poker, run long distances, and scuba dive.
Investment focus areas
— Enterprise Software
At the earliest stage, I look for exceptional founders with relevant superpowers for accomplishing their company’s missions. The founders I work with have many different superpowers — they might be a world-class storyteller, team builder, enterprise sales genius, or technical superstar. However, all of them are intellectually honest and curious, with a willingness to learn, an ability to hire others with complementary skills, and the strategic acumen to identify risks and iterate towards a winning approach. My favorite conversations are with clearly-obsessed founders who enjoy being asked the tough questions and who can walk through their plan A through plan Z in response.
As a product is built and the company becomes larger, I need to get a sense for business health and growth. Some things we look for generally are large or rapidly growing markets, logical “why now” arguments, stellar product feedback, and economics that make sense depending on the company stage & plan. I’m particularly excited about the increased accessibility and performance of LLMs & other generative models to transform many software verticals, as well as products that empower developers and practitioners working with data or machine learning models. Given my background as an engineer & PM, I enjoy chatting with founders rethinking the high barriers to build & maintain software and ML.
During my last year working at Scale, I started angel investing when I saw a lot of my friends and second-degree network started companies. I was surprised at how much I loved reliving the journey from 0 to 1, reminiscent of my early days at Scale years ago, and I found a value-add angel strategy through helping my portfolio with product feedback, technical hiring, & ML stack expertise. Now as a VC, I still think it’s unmatched to be a part of the journey as early as possible. I love truly partnering with my portfolio companies through helping them hire their first engineers, launch an MVP and get customer feedback, close their first customer contracts, and tackle the inevitable new challenges that come with scaling past that.
I started doing competitive math in middle school because my small-town public school in Alabama offered an advanced math class that included it. I very quickly discovered that I loved competition and problem solving. I also learned that to have a shot at competing nationally, I needed to go outside of school - first to a math group an hour away from home, and then to out-of-state competitions, camps, boarding school, and eventually college. Probably most significantly, I met Alexandr Wang, the Scale founder & CEO, through math competitions. After playing poker together on the poker club at MIT, I ultimately joined Scale as one of the first engineers & became their first PM. In addition, many founders & operators I work with most closely today originally came from my broader math competition network.
Due to my passion for competitive math growing up, I wanted to be a math researcher when I started college. However, I quickly got side tracked, as I loved my computer science & especially AI (the perfect combination of math and CS) classes in college. I decided to join Scale as an engineer to work with customers on the cutting-edge of machine learning - a perfect fit.
As early engineer and PM at Scale, I got a sense for how challenging scaling infrastructure at a startup is, as well as how challenging it is to deploy ML today, both through our internal ML deployments and hearing about our customers’ challenges. I realized so many more awesome startups could be built if we had better developer, data, & ML tooling.
I also saw self driving cars improve - an incredible feat of deep learning - through Scale’s data annotation products. Later, as an angel and then VC, I saw the opportunities that LLM advances can unlock across verticals, whether as a new virtual personal companion or professional productivity assistant that understands & generates text or code. The time is now for LLMs & other generative models to rethink inefficient legacy software workflows across healthcare, law, visual media, sales & marketing, supply chain, finance, and many other industries. I’m so excited to partner with new AI-first startups as well as existing companies who take advantage of their current product and distribution to best utilize this new technology.