Founder Interviews: James Wu and Allen Lu of AdaptiLab

Childhood friends James and Allen are making it easier for companies to build machine learning teams with AdaptiLab (Techstars Seattle ‘19).

What’s your background, and what are you working on?

James: Hi I’m James, the CEO of AdaptiLab. I worked on machine learning at two startups over the last three years before co-founding AdaptiLab, and I was a 2017 Freestyle Venture Capital Engineering Fellow. I’m a Seattle native.

Allen: Hi I’m Allen. I am the CTO of AdaptiLab, and I’m also from Seattle. I worked on deep learning teams at Google and Microsoft before co-founding AdaptiLab, and I am a published deep learning researcher.

James: AdaptiLab builds software that helps companies grow their machine learning teams from hiring to productivity. Our initial product is an automated technical screening tool that sits at the top of hiring managers’ interview funnels. The product automatically generates coding assessments that are customized for the company’s domain and tests candidates on core machine learning competencies: data preprocessing, data analysis, feature engineering, model development/inference, and general algorithms. We also automatically grade candidate’s submissions and provide technical score reports. We’re a recent Techstars Seattle graduate.

What motivated you to get started with your company?

James: Machine learning is transforming industry at an unprecedented rate. However, companies are adopting the new technology so quickly that the infrastructure for hiring and developing machine learning talent is universally lacking. We’ve spoken to over 100 hiring managers and lead engineers and confirmed every company — regardless of size and vertical — makes mistakes when trying to hire machine learning talent.

The cost of failure is painfully high. Companies spend hundreds of thousands trying to build a machine learning engineering team. Major costs drivers are decreased engineering productivity in interviewing (companies average 50+ interviews per hire), sourcing qualified candidates, and high churn due to skillset misalignment.

Our software product for evaluating talent will help hiring managers improve the quality of their candidate funnels, reclaim their engineer’s time, and ensure candidate skillset fit.

Allen: Both James and I are machine learning engineers by training (Wu studied computer science and statistics at Duke University, and Lu studied computer science, machine learning, and language technologies at Carnegie Mellon), and we had experience working with machine learning in industry. Before building AdaptiLab we built the most viewed course for machine learning on Educative (a leading platform for developer education). We knew we had the right backgrounds to tackle this problem, and we built a solid team of mentors and advisors from Techstars to help.

What went into building the initial product?

James: We started by conducting a lot of customer discovery. We made a list of our target customer’s biggest pain points and identified the features we would need to launch an MVP. This included an interview environment with automatic code grading and a hiring manager dashboard to send out interviews to candidates and receive score reports. We didn’t spend too much time designing extra features or beautifying the UI for the hiring manager side because we wanted to get the product in the hands of customers as soon as possible.

Allen: We launched the first version of the product at the end of March and since then we’ve been making regular feature deployments and updates based on our customer’s feedback. For instance, we’ve improved the candidate interview experience, the quality of the generated technical score reports, the functionalities included in the hiring manager dashboard, and the question generation system for higher quality questions.

Candidate Interview Environment (from AdaptiLab)

How have you grown your company since your launch?

Allen: We have a wide market because so many different industries are trying to adopt machine learning. Our product also works for more than just machine learning engineers. We help companies evaluate most data-oriented technical roles, such as data analyst, data scientist, business intelligence engineer, deep learning engineer, applied scientist, etc.

We chose to primarily target fast-growing tech companies when we launched because they have the fastest adoption rate and the heaviest need for our product. Typically these companies are building out dedicated machine learning teams and have at least five open positions they’re trying to fill at any time. Our product is currently being used by hiring managers at fast-growing tech companies and enterprise giants.

James: We also recently graduated from Techstars Seattle and raised a $1.8m seed round led by Trilogy Equity Partners. We plan to use the funding to help meet customer demand and accelerate our go-to-market. We will grow our engineering team to improve and refine our product and build a growth team to scale to more customers. We have a team of 4 at the moment, with plans to grow to 10 by 2020.

Have you found anything particularly helpful or advantageous?

James: I think we have some mutual character traits that have been very helpful in building AdaptiLab quickly. We are both open-minded and flexible when it comes to trying new ideas and pivoting away from old ones that prove to be ineffective. This is crucial for a startup because so much of building a successful product is the process of getting feedback from users and iterating on the product.

Allen: We’re also both organized. When first starting a company, there are a lot of logistics issues that need to be taken care of even though the main focus should be building the product and then selling it. We budget our time well to make sure we cover all the necessary legal and finance tasks, without letting them distract us from the main objective, which should always be building the product and selling it.

James: For a pair of first-time founders, the Techstars Seattle experience provided an incredible amount of mentorship and resources. Many of our mentors during the program are now advisors for our company or strategic investors. The program forced us to be thoughtful about how we built our product, executed our go to market strategy, and told our company’s story for investors and potential hires. We think a well-respected accelerator, such as YC or Techstars, is worth the time and equity, especially for teams that are lacking experience in some business function (e.g. engineering, go-to-market, etc). We also had the benefit of participating in our hometown’s accelerator, so we were able to maintain our networks within the Seattle community.

Allen: We still have access to many of the connections we built during our time in university. Most startup founders at best only utilize their university’s alumni network, but we’ve also sought advice and assistance from former professors and research advisers as well as spoken to students directly.

What’s your advice for entrepreneurs who are just starting out?

James: There are so many great resources out there for new entrepreneurs to build a company. You should try to read and learn as much as you can and then create your own thesis for how you will run your company. Many of these resources are completely open-source/free as well. We’ve personally enjoyed The Lean Startup, The Hard Thing about Hard Things, and Venture Deals.

Try your best to find a co-founder and make sure at least one member of the founding team has a technical background because it enables the company to build and test ideas quickly. Pick a co-founder that you trust and that has a similar passion for the space you’re in. The founding team will learn together, share hardships, and constantly improve each other and the company. Allen and I have been building products together for over 5 years and we’ve known each other for more than double that, so we can build on each other's strengths and communicate effectively.

Allen: Always think about the customer. As a pair of technical founders, we had to learn to not over-index on the product and remain focused on solving the real world pain points we were hearing in our customer interviews. Launching as fast as possible and iterating with new product versions helps drive this focus.

Where can we go to learn more?

Check out our product at www.adaptilab.com. You can also follow our Linkedin for news. If you would like to connect with the co-founders, you can find us on Linkedin as well: James, Allen. We’re always happy to hear feedback from users and answer any questions regarding the company.

This interview is brought to you by OneUp, a tool to schedule and automatically repeat your posts on Facebook, Instagram, Twitter, Pinterest, LinkedIn, and Google My Business.