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Josh Brenner, CEO of Hired – Interview Series

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Josh Brenner is the CEO of Hired, the leading AI-driven hiring marketplace that matches talent with top companies such as Instacart, Wayfair, Zendesk, Capital One, and Peloton. works a bit differently than Monster and other employment platforms, could you share the concept of how companies apply to interview candidates?

Hired is unique as it brings together three things to transform the hiring experience: a highly-curated, data-driven hiring solution specializing in tech and sales talent, high-touch customer service, and the support of The Adecco Group global network. Companies can sign up through our platform in a multi-step process, which involves specifying relevant roles and location – which can also be remote – and preferences such as years of experience or base salary that they are seeking to hire for. We then pair employers with a dedicated talent success manager and provide access to hiring data and analytics to help them surface pre-vetted and highly-qualified candidates that are a fit for their role requirements. To create a personalized and curated experience, we relay relevant candidate information, such as their skills, work experience, and preferred salary back to employers to encourage them to send interview requests to those who are a suitable match. The candidates we feature are actively seeking new opportunities, often getting several interview requests in the first few days from signing up, so we encourage employers to act quickly and transparently.

By providing a data-driven hiring solution that accounts for the specific needs of both employers and candidates, we improve the quality of matches, reduce time-to-hire, and ultimately make it as easy as possible for companies to build amazing, diverse teams.

Could you discuss some of the machine learning methodologies that are used to match employers with candidates?

Our machine learning models track real-time hiring data, monitor trends, and predict hiring behavior to more quickly and accurately match employers to candidates. Having this vast amount of relevant, real-time data from our platform with over 17,000 employers and over 3 million candidates allows us to provide highly-curated and personalized matches, leading to a better quality of matches and higher acceptance rates.

There is also an ever-growing need to understand and explain the predictions of machine learning algorithms used in hiring for transparency, bias mitigation, and to provide recourse to users.

At Hired, we leverage machine learning to provide recourse to help candidates understand why they were not approved on our marketplace and how they could improve their profile. We help candidates increase their chances of approval by providing actionable feedback, which can range from suggesting they add a forgotten skill to implementing long-term changes like learning a new programming language.

Since existing methods for recourse for machine learning lack speed, actionability, or fail to reliably find changes that would flip the decision of the system, we developed our own recourse method for machine learning systems. We also actively mitigate bias in our machine learning systems through oversampling techniques that ensure that a candidate's race, gender, or age does not impact the decision of the algorithm.

Employers benefit from this system as well since valuable candidates could have been overlooked by our scoring system if it didn’t provide this actionable feedback to candidates, leading to recruiters missing out on great candidates.

How does AI help to reduce time-to-hire at scale for companies?

We leverage AI and real-time data from our platform to provide highly accurate matches to employers faster and more efficiently. Through surfacing candidates that are specifically tailored to the company’s job requirements, we save recruiters an average of 45 sourcing hours per hire, which would be otherwise dedicated to pre-screening interviews, reviewing resumes of unqualified candidates, and contacting unresponsive, passive candidates. Since we focus on identifying and promoting active candidates on our platform, we are able to reduce time-to-hire by at least 4x the average and help companies scale their teams with highly qualified talent. For example, we’ve worked with Capital One to help them recruit over 300 candidates through the Hired marketplace, saving them countless hours sorting through resumes.’s recent report “The Hired 2021 Impact Report – Wage Inequality in the Workplace“, focused on racial and gender inequalities in the workplace. What were some of the key findings and did any of these surprise you?

Our data has found that while progress is being made and the wage gap is narrowing, we still have a long way to go. In 2020, men were offered higher salaries than women for the same job title at the same company 59% of the time, compared to 65% in 2019. Specifically, companies offered women 3% less on average than men for the same roles in 2020, compared to 4% in 2019. Black candidates saw wages that were 4% lower than the baseline in 2020 vs. a gap of 5% in 2019.

We continue to see an ongoing trend where underrepresented groups who are paid less also expect lower salaries than their white, male counterparts – even if they have the same experience. Race contributes significantly to this expectation gap, as low wage expectations are most prevalent in minority women compared to white women or minority men. For example, Black women expect salaries that are 10% lower than those of their white male counterparts.

Underrepresented groups can fall into a repetitive cycle of receiving lower pay if they do not have transparency into how much they deserve in compensation for their role. We’ve found that the best way to narrow the wage gap is by increasing salary transparency, so that all candidates are aware of the pay they deserve and empowered to demand equitable pay. This is what we aim to drive forward with our annual reports.

While pay transparency has been a long-withstanding issue for all employees, regardless of demographics, we were surprised to find that younger employees are more likely to ask for, and receive, more equal wages than in past generations. This is a positive development because employees who learn to negotiate salaries at the early stages of their career increase their chances of being paid fairly down the line. Continuing to increase wage transparency and reducing the expectation gap for entry-level employees could have a profound, long-term impact on wage inequality as a whole and influence fairer compensation for generations to come.

How does remove unconscious bias when hiring?

To mitigate unconscious bias influencing hiring decisions, we’ve equipped our platform with specific tools and capabilities that foster transparency, efficiency, and equity, such as:

Actionable, unbiased feedback –  When offering feedback to candidates on how they can improve their profile to be accepted to the platform, such as adding a specific skill, Hired verifies that, if made aware of candidates’ race, gender, or age, the system does not suggest changing for example one’s race to change the decision of the algorithm.

Salary alerts – To reduce the likelihood of unequal compensation, Hired alerts both employers and candidates if they receive or request a salary significantly above or below the average for their position and level of experience. Our salary bias alerts have resulted in companies’ offer salaries changing 4.3% of the time, with the average salary adjustment being a 20K difference.

Salary transparency – Compensation is provided transparently, both from the perspective of the candidate, indicating their expected salary when joining our platform, and the employer when reaching out to a candidate.

Bias reduction filters – Our platform offers the ability to mask candidates’ demographic details, to reduce unconscious bias, and encourage companies to hire candidates on the basis of their skills.

Customized assessments  – As a hiring marketplace, we focus on skills based matching including the use of skills assessments to further promote capabilities rather than a traditional resume and job application process. As these assessments are remotely accessible, we enable companies to surface skilled candidates located anywhere across the globe, and diversify their talent pipeline.

What type of companies are currently recruiting using the platform?

Hired has over 17,000 employers on its marketplace, including many well known brands such as Instacart, Wayfair, Zendesk, Postmates, Twitch, Capital One, Compass, and Peloton. Specifically, our platform is used by the company’s hiring managers, recruiters, HR departments, and C-level executives to help them access our pool of highly-qualified and curated candidates.

What type of educational or employment history requirements are needed from candidates to use the platform?

Candidates on Hired need to be in the areas we specialize in including software engineering, analytics, product management, design, QA, and sales roles. Job seekers of all levels are encouraged to sign up, as our platform highly prioritizes skills-based hiring, regardless of educational background. When creating a profile, candidates are prompted to outline their work history, skills, and salary requirements. Those who have demonstrated considerable experience in their desired roles through internships or open source work, may also include these projects under their years of experience. We also encourage candidates to leverage skill-building resources, such as coding bootcamps and educational courses, to improve their chances for approval and ultimately securing interviews and job opportunities.

Why should AI/machine learning professionals use

Hired provides AI and machine learning professionals with access to the most innovative companies in 18 of the leading tech hubs across the U.S., Canada, UK, and Ireland. We understand that job hunting in a competitive field can often feel like a frustrating process, which is why we aim to empower candidates, by connecting them with employers who are a fit to their unique skills, experiences, goals and values.

We also offer various resources for AI and machine learning professionals, such as our annual State of Software Engineers report. As skills are becoming more important than pedigree, our report outlines the most in-demand skills and coding languages based on our platform data, and provides insights into how AI and machine learning engineers can differentiate themselves by upskilling and leveraging coding boot camps and self-education programs for example. Our recent report found that in the major U.S. tech hubs, machine learning engineers salaries range between $115k/year to $171k/year on average, providing professionals with valuable salary  insights based on location, role, and experience.

Is there anything else you would like to share about

Hired’s vision is to make all hiring equitable, efficient, and transparent. We aim to achieve that vision by empowering connections between ambitious people and teams, using technology and human empathy to reinvent the way people hire and get hired.

In the past, recruiting has tended to be very transactional between the employer and candidate. Yet as people increasingly seek companies that align more with their personal values and professional development goals and employers recognize how central talent is to business growth, there’s a clear need for the hiring process to be more digital and data-driven yet customized. Especially with the increasing adoption of remote work, we already see a shift to more digital hiring solutions that empower both employers and job seekers with the most efficient and effective way to hire across the globe. We’re excited to be at the forefront of this shift and continue to drive forward our vision of transforming hiring.

A founding partner of unite.AI & a member of the Forbes Technology Council, Antoine is a futurist who is passionate about the future of AI & robotics.

He is also the Founder of, a website that focuses on investing in disruptive technology.