Machine Learning Engineer
Triplebyte helps companies find and hire great technical talent. For any company building software, this is crucial for success. A recent survey of top C-Level Executives, across multiple industries, showed that software, R&D and recruiting technical talent are the top areas they are planning increased investment and budget over the next 5 years. Companies like Apple, Dropbox and American Express trust Triplebyte’s online technical assessment to identify the best engineers for their open roles and reduce the time and effort it takes to hire them.
We have built Machine Learning models that predict the likelihood of an engineer getting an offer from a particular company. Interviewing and assessing engineering talent tends to be noisy, but our technical assessment has proven to be good enough to extract meaningful signals. We collect data on interview results and in-house evaluations, which gives us a unique dataset to play with. We've used that data to deliver a 40% conversion rate on our candidates at interview to offer, compared to the industry standard 20%.
Our mission is to create a scientific method for identifying great talent and intelligently route it to the best place, streamlining and speeding up the recruitment process, while removing human biases that can hold back some candidates.
You can read more about our company and hear from our founders in the press here:
You can also read some 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.
We're an experienced team, the founders have each built and sold companies before. Ammon and Guillaume founded Socialcam (acquired by Autodesk for $60 million) and Harj was the first partner hired at Y Combinator 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.