Sales Development Representative

Sorry, this job was removed at 08:02 p.m. (CST) on Monday, Jul 28, 2025
Easy Apply
2 Locations
In-Office
68K-85K Annually
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role

Scale is looking for a self-starter SDR with a demonstrated track record of top performance, ability to receive and incorporate feedback, and excitement to grow a career in the Machine Learning and Artificial Intelligence space.

This position will report to the Head of Enterprise Business Development and will be responsible for outbound prospecting, identifying, and qualifying sales opportunities for the Enterprise Sales team.

You will:

  • Generate new business opportunities by prospecting, researching, and outbounding into some of the largest Enterprise accounts.
  • Ability to write highly tailored and personalized cold outbound messaging to book meetings with target personas.
  • Conduct high level qualification conversations with Senior Executives in target accounts.
  • Effectively communicate the value of Scale and our products to potential customers.
  • Align and collaborate with stakeholders across sales, product, and marketing.
  • Achieve or exceed monthly and quarterly quotas of qualified opportunities.

Ideally you'd have:

  • Bachelor’s degree or equivalent work experience.
  • 1-2+ years of sales experience in a fast-paced Enterprise software or technical environment.
  • Track record of consistent top performance.
  • Excellent outbound phone and email communication skills.
  • Extremely flexible with an ability to multitask, prioritize, and manage time effectively.
  • Experience prospecting into large technical enterprise organizations.
  • Process oriented, organized, and able to work well in unstructured environments

Nice to haves:

  • Experience in AI, Computer Vision, or SaaS technologies.
  • Self motivated, persistent, and resilient mindset.
  • Collaborative team player who is open to feedback and coaching.
  • Excitement for sales and friendly competition.
  • Experience with Salesforce, Outreach, LinkedIn Sales Navigator, and ZoomInfo.

Benefits:

  • Competitive pay
  • Hybrid work
  • Flexible PTO
  • Health, Dental & Vision Coverage
  • Commuter Benefits

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.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:
$68,000$85,000 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 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

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

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The Company
San Francisco, CA
523 Employees
Year Founded: 2016

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.

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