Software Engineering Daily

Engineers who start companies often find themselves building something they have no experience with: a hiring process.

Hiring engineers today is not as systematic as building software. We don’t have lots of data that tells us what makes for an effective programming interview question. The smartest tech companies in the world are still making hiring mistakes–often through the “false negative” of rejecting candidates who did not do well in their interview process or through the “false positive” of hiring candidates who did well in the interview, but were not a good fit for the job.

If you are a hiring manager or a company founder, you will eventually have to build a hiring process. If you don’t treat that hiring process scientifically, you will likely make some mistakes.

Ammon Bartram has conducted more than 1000 interviews with engineers, accumulating a vast amount of data. This data was gathered deliberately and scientifically, through closely tracked interview questions and a consistent end-to-end process for the job candidate. Ammon joins the show to talk about the data set he has accumulated, the conclusions from all of these interviews, and how engineering organizations can use this data to develop a smart, data-driven hiring process.

Ammon is co-founder of Triplebyte, a company that helps match engineers and tech companies. Triplebyte also publishes lots of research and blog articles about conducting good interviews, developer salaries, and bootcamps vs. computer science degrees. Full disclosure: Triplebyte is a sponsor of Software Engineering Daily. (However, Ammon has been a guest several times before on the show, since before Triplebyte was a sponsor, and I always enjoy getting to talk to him.)

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Background checks are a routine part of the hiring process. After a potential employee has made it through job interviews, a background check is administered to look through the applicant’s work history, criminal record, and other available data. Conducting a conventional background check can require manual work–including phone calls for reference checks and going to a courthouse to look up physical records of a person’s criminal history.

The on-demand economy has rapidly increased the volume of workers who are getting hired–and all of them need background checks. Lyft drivers, DoorDash food delivery people, Instacart shoppers–these on-demand workers are being trusted with our lives. We get into their cars, let them into our houses, and eat the food that they hand to us. We want some guarantees about their reputation.

Checkr is a background check platform that allows companies to request background check services via API request. Checkr was started 4 years ago, and has benefitted from the growth of gig economy services like ridesharing and food delivery.

Since the background check API product has found success, Checkr has raised additional capital and invested in other new products: a next-generation background check product based on machine learning, and a mobile app that allows people to instantly background check themselves and find jobs that align with the results of that background screening.

Tomas Barreto is the VP of product and engineering at Checkr and he joins the show to describe how the core Checkr API product works, and the challenges of automating the background check process. We also explored the product development roadmap for Checkr, and the product opportunities that come from building within a specialized vertical such as background checks.

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Engineers need to have an awareness of the business model that allows their company to succeed. When a software company is going to market, the engineers need to work closely with the sales and marketing team to formulate a strategy for building and selling that product. This is especially true in highly technical products, such as database- or platform-as-a-service companies.

An engineer at a Hadoop-as-a-service product needs to work with the sales and marketing team to explain why a customer might want a data platform. An engineer at a SaaS company needs to understand how the cost to provide a service might scale, so that the sales team can decide on appropriate pricing.

Mitch Ferguson has been developing businesses at software companies since the 90s. He helped build out SpringSource and arrange the acquisition of SpringSource by VMWare–an acquisition that later enabled the creation of Pivotal Software. He then joined Hortonworks as an early member of the team bringing their Hadoop platform to market.

Today Mitch works as a co-founder of Accel G2M, an organization that helps bring technology companies to market–building out their sales, marketing, product, and organizational strategies.

Accel G2M

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For most of history, a typical musician would learn to play one specific instrument. As synthesizers became available to the public, it became commonplace for a musician to create their own instruments using hardware and software. By the early 2000s, digital audio workstation software allowed a musician with a laptop to have access to the tools of a record producer. These tools changed how music is made, and gave rise to new genres.

Creating electronic music on the computer is a practice much like software engineering. Iteration, modularity, and software architecture skills are required to build a song intelligently. Music engineering also requires working at numerous levels of abstraction: the synthesizer level, the song arrangement level, the mixer level, and the design of melodies.

Dom Kane is a musician and sound engineer who writes music for mau5trap, a label started by deadmau5. He has built software synthesizers, worked with numerous artists as a producer, and written music for film and TV. He joins the show to talk about working as a professional electronic musician. We also talk about the overlap between engineering and the different facets of crafting modern music on the computer.

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