Scale’s Enterprise Applications business is growing faster than ever in the quest to develop reliable AI systems for the world’s most important decisions. As an AI Deployment Strategist you will be right at the center of it, helping our largest customers build, launch, and scale groundbreaking GenAI solutions that redefine their industries. You will take full ownership of our deployment success, turning bold ideas into real impact and ensuring every engagement delivers exceptional results.
In this role, you will work directly with data, engineering, business and AI leaders at some of the world’s top Enterprises. You will guide projects from the first conversation through delivery, expansion, and measurable business outcomes. Within Scale, you will collaborate closely with Machine Learning, Engineering, and Finance teams to streamline execution, improve processes, and shape our approach to enterprise delivery.
This is a fast-moving, high-impact role that blends customer strategy, operations, product strategy and execution. You will lead the team that serves as the voice of the customer and ensure their flawless execution of customer projects.
You will own:
- Client Leadership & Growth: Own relationships with key enterprise customers and ensure every engagement runs smoothly, delivers value, and grows over time.
- Flawless Delivery & Execution: Manage delivery across multiple workstreams, ensuring timelines, quality standards, and SLAs are met.
- Strategic Account Expansion: Identify and pursue new opportunities within existing accounts that drive measurable business impact.
- Cross-Functional Problem Solving: Collaborate with EPD, Finance, and Operations to remove blockers, drive efficiency, and continuously improve how we deliver.
- Strategic Feedback Loop: Synthesize and communicate insights from customer engagements to help inform the long-term product roadmap and strategic direction.
- Mastering Complexity & Ambiguity: Bring structure, clarity, and momentum to complex projects in dynamic environments.
Ideally, you’d have:
- 5+ years of work experience in high-growth, high-ambiguity environments. Successful candidates have had experience in MBB consulting, banking or private equity or as a technical product or program management role in the tech industry, but we are open to alternative profiles.
- A technical background (education or professional experience within CS, Economics, Statistics, Engineering or STEM field)
- Exposure to GenAI solutions via your current role or personal projects
- A proven track record in building and expanding client relationships
- Ability to understand the ML concepts and build great relationships with technical customers
- Great cross-functional experience and collaborative ability
- Excellent verbal and written communications
- A track record of structured, analytics-driven problem solving
- A history of diligence and organization across multiple work streams
- An action-oriented mindset that balances creative problem solving with the scrappiness to ultimately deliver results
- Willingness to travel up to 50% depending customer and deployment needs
Nice to have:
- Exposure to enterprise AI or data infrastructure products.
- Deeper industry knowledge in healthcare, consumer, financial services
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 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, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. 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.
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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.
