Jobs
Required skills
Python
Machine Learning
Cloud platforms
AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the usage of frontier data and models by building complex agents for enterprises around the world through our Scale Generative Platform (SGP).
The SGP ML team works on the front lines of this AI revolution. We interface directly with clients to build cutting edge products using the arsenal of proprietary research and resources developed at Scale. As an Applied AI Engineer, you’ll work with clients to create ML solutions to satisfy their business needs. Your work will range from building next-generation AI cybersecurity firewalls to creating transformative AI experiences in journalism to applying foundation genomic models making predictions about life-saving drug proteins. Daily data-driven experiments will provide key insights around model strengths and inefficiencies which you’ll use to improve your product’s performance. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!
You will:
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Own, plan, and optimize the AI behind our Enterprise customer’s deepest technical problems
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Leverage SGP to build the most advanced AI agents across the industry including multimodal functionality, tool-calling, and more
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Have experience gathering business requirements and translating them into technical solutions
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Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
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Push production code in multiple development environments, writing and debugging code directly in both our customer’s and Scale’s codebases
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Be able and willing to multi-task and learn new technologies quickly
Ideally you'd have:
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A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client’s business goals
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Strong engineering background: a Bachelor’s degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background.
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Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model results
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Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
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Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)
Nice to haves:
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Strong knowledge of software engineering best practices
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Have built applications taking advantage of Generative AI in real, production use cases
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Familiarity with state of the art LLMs and their strengths/weaknesses
**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 accommodations@scale.com. 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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About Scale AI

Scale AI
Series CAccelerate the development of AI applications.
501-1,000
Employees
San Francisco
Headquarters
$7.3B
Valuation
Reviews
3.7
10 reviews
Work-life balance
2.8
Compensation
3.5
Culture
3.9
Career
3.2
Management
2.6
65%
Recommend to a friend
Pros
Innovative and interesting work/projects
Supportive team and colleagues
Learning environment and mentorship opportunities
Cons
Fast-paced and stressful environment
Poor management and lack of direction
Long hours and heavy workload
Salary Ranges
3 data points
Junior/L3
L3
Junior/L3 · Data Scientist L3
0 reports
$123,049
total per year
Base
-
Stock
-
Bonus
-
$104,592
$141,506
Interview experience
5 interviews
Difficulty
3.2
/ 5
Duration
14-28 weeks
Offer rate
20%
Experience
Positive 20%
Neutral 60%
Negative 20%
Interview process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Technical Interview
5
Final Round
Common questions
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
System Design
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