Scale’s Talent Acquisition team is an integral part of the organization’s success. In this role, you will partner directly with executives (up to the C-suite) and other leaders on Recruiting, People, Finance, and IT. You are a data-driven builder and an innovator: unafraid to try new things, build new processes and consistently innovate existing ones, and test creative new strategies. You understand when specific processes no longer serve as intended and can see around corners. You thrive on details and operational excellence and hold a high bar for yourself, your peers, and your team. You are excited about setting the tone of our recruiting strategy and being a key part of the leadership team.
You will:
- Lead a team of program managers and talent acquisition professionals to maximize efficiency, enablement, and planning for our global recruiting efforts
- Partner with cross-functional leaders globally and oversee headcount management, recruiting enablement, talent marketing, candidate experience, recruiting insights, and early career programs
- Understand business priorities & objectives; help build a high impact, max velocity hiring strategy that addresses key gaps between the talent in place today and the talent required to drive business success
- Iterate and advance our talent acquisition capacity model to most accurately inform optimal allocation of resources to meet hiring needs across the global workforce
- Create a best-in-class talent marketing strategy, positioning Scale as an employer of choice for both early career and tenured professionals
- Bolster and nurture the pipeline from our inbound and referral channels
- Drive the strategy for the recruiting coordination team, ensuring both a seamless experience for candidates and interviewers across our global offices and remotely
- Be committed to presence in the SFHQ office twice a week, we are a hybrid workplace (we also have offices in New York, Washington DC, Saint Louis, Mexico City, and Europe)
Ideally you'd have:
- 8+ years of experience in Talent Acquisition and 5+ years managing TA Operations
- 4+ years of experience managing managers
- Extensive experience managing operations on Talent Acquisition efforts that support >100 hires per quarter
- Demonstrated experience partnering with senior level stakeholders, up to the executive level, on global company-wide initiatives
- A data-driven approach to decision-making that leads to increased operational effectiveness & efficiencies with a keen ability to explain data insights to talent & business partners of all levels
- A business mindset and understanding of how your impact in talent directly translates to the greater success of the organization
- Demonstrated passion and ability for leading, motivating, and coaching teams of ICs and Managers
- An expert with our Talent Acquisition tools suite such as Greenhouse, Tableau, Google Analytics, Gem, LinkedIn Recruiter, Modernloop (nice to have) at an admin-level access
- A focus and commitment to building diverse teams
Nice to haves:
- Previous startup experience
- Hands-on SQL ability
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
The base salary range for this full-time position in the location of San Francisco is:
$176,000—$211,200 USD
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an affirmative action employer and inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
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What We Do
Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.