Triplebyte is transforming the way software engineers are hired. Our mission is to build an open, valuable and skills-based credential for all engineers. This is important because millions of people have skills (and deserve good jobs), but don't fit the profile that recruiters seek. Another way of saying this is that talent is uniformly distributed, but opportunity is not. Our goal is to broaden the distribution of opportunity.
To do this, we have built a background-blind technical assessment and interview process, and we use it to find engineers and help them get jobs at 450+ top companies. Our rich understanding of candidates' skills and propriety machine learning models enable us to find the right match between our candidates and partner companies. This is why companies like Apple, Dropbox and American Express trust Triplebyte's technical assessment to identify the best engineers for their open roles and reduce the time and effort it takes to hire them.
We just raised a $35 million Series B ( https://techcrunch.com/2019/04/11/triplebyte-raises-35m-for-its-online-coding-test-and-credentialing-service-for-hiring-engineers/ ) and our team of 65 is growing quickly! Now is a great time to join as we're on an exciting growth trajectory. You will have lots of opportunities for taking on responsibility and developing new skills quickly.
You can read more about our company and hear from our founders in the press here:
Triplebyte raises $35M for its online coding test and credentialing service for hiring engineers ( https://techcrunch.com/2019/04/11/triplebyte-raises-35m-for-its-online-coding-test-and-credentialing-service-for-hiring-engineers/ )
Triplebyte raises $35 million to match engineers with employers ( https://venturebeat.com/2019/04/11/triplebyte-raises-35-million-to-match-engineers-with-employers/ )
CNBC (Your Next Job Interview Could be with a Robot) ( https://www.cnbc.com/2018/10/03/future-of-jobs-your-next-job-interview-could-be-with-a-robot.html )
Lessons from Doing Y Combinator Twice - Harj Taggar ( https://youtu.be/vaRrq0qWAkQ )
Building an Engineering Team - Harj Taggar and Ammon Bartram ( https://www.startupschool.org/videos/44 )
You can also read some case studies ( http://triplebyte.com/company-case-studies/ ) with a few of our partner companies like Box, Instacart, Mixpanel and Gusto and also learn more about us on our press page ( http://triplebyte.com/press ).
We're an experienced team ( http://triplebyte.com/about ), the founders have each built and sold companies before. Ammon and Guillaume founded Socialcam (acquired by Autodesk for $60 million ( http://www.inc.com/eric-markowitz/socialcam-just-18-months-old-is-acquired-by-autodesk.html )) and Harj was the first partner hired at Y Combinator ( https://techcrunch.com/2010/11/12/y-combinator-names-first-new-partners-since-2005-paul-buchheit-and-harj-taggar/ ) since its founding.
Triplebyte screens and evaluates thousands of engineers per month to find the best candidates for our partner companies. Human decision making doesn't work at our scale; our marketplace is powered by automated assessment and decision making. Triplebyte has three cornerstone ML products: our quiz, our interview, and our matchmaking. As a machine learning engineer, you'll be responsible for the end-to-end process of designing and running experiments to serving production models at scale. Some of our pipelines use off the shelf components, but we're also implementing custom models and techniques from the latest research papers. We're also building forecasting tools for internal teams to measure and predict outcomes. This is an ideal role for an engineer or data scientist who wants the scope and responsibility to own features/products from the inception and research phase through to measuring real-world results.
Fields your work will touch on
- Recommender systems
- Time series analysis
- Survival analysis
- Bayesian inference
- Probabilistic programming
- Robust exploratory/experimental skills. We have a novel dataset of candidate profiles and interview outcomes from our candidate screening process and our hiring marketplace. You'll be responsible for designing and evaluating experiments to predict downstream outcomes.
- Ability to implement models from research. Some of our best improvements in both speed and predictiveness has come from doing literature surveys and implementing novel techniques from research papers.
- Engineering skills. This is a hybrid research/engineering role. You'll be responsible for productionizing your pipelines/models and integrating against our back-end services.
Compensation and Benefits
Competitive salary and stock options package
Open vacation policy
Employer paid health, vision and dental insurance
401(k) plan with matching
Pre-tax commuter benefits
Daily catered lunches
We believe strongly in building a truly meritocratic, unbiased process for finding great talent. Even the best technology companies today still use where people went to college as a proxy for intelligence and ability. We're building a process that looks only at ability, not credentials, so we can have a future where everyone can focus on just learning and being good at what they do, not how they look on paper.
Every aspect of running a company has been improved over the last decade, except hiring. Most decisions are still made using amorphous terms like "gut feel" or "culture fit". They should be made using crisp data. Only a company specializing on this problem, using data collected from the hiring process at hundreds of companies, can solve it. That's the company we're building. Our mission is creating a scientific method for identifying great talent and intelligently routing it to the best place. Starting with software engineers.
The Company is an equal opportunity employer and makes employment decisions on the basis of merit and business needs. The Company does not discriminate against employees or applicants (in any aspect of employment, including, but not limited to recruiting and hiring, job assignment, compensation, opportunities for advancement, promotion, transfers, evaluation, benefits, training, discipline, and termination), on the basis of any characteristic protected under applicable federal, state, or local laws.