As a member of our Frontier Tech Consultant team, you will play a critical role in advancing cutting-edge AI innovations by conducting high-impact experiments and ensuring seamless execution at the highest quality standards. Your work will directly contribute to Scale AI’s growth, shaping the future of artificial intelligence.
In this role, you will be working on various types of projects, including but not limited to: research experiments, dataset generation, data quality improvements, and in-depth technical analysis. You will tackle complex, technical and operational challenges while collaborating closely with Scale’s ML research scientists and SPM team.
The ideal candidate is analytical, detail-oriented, and results-driven, with strong problem-solving abilities and excellent communication skills. We are looking for someone who thrives in a fast-paced environment, is proactive in overcoming challenges, and is committed to delivering exceptional outcomes. If you are eager to contribute to the forefront of AI innovation, we encourage you to apply.
You will be responsible for:
- Design and execute research experiments
- Build and evaluate frontier LLM datasets
- Develop training and testing material for frontier pipelines
- Improve quality of existing and new products
Ideally you’d have:
- Strong machine learning knowledge, either by being in the final years of a ML PhD career or having already graduated
- Strong writing and verbal communication skills
- An action-oriented mindset that balances creative problem solving with the scrappiness to ultimately deliver results
- Analytical, planning, and process improvement capability
- Experience working in a fast-paced, entrepreneurial environment
- Technical skills including familiarity with Python, GPU, AWS, API, LLM, ML, and SQL
Pay: $60-80/hr
Commitment: This is a fully remote, US-based part-time (10-20 hours per week), on-going contract position staffed via HireArt.
HireArt values diversity and is an Equal Opportunity Employer. We are interested in every qualified candidate who is eligible to work in the United States. Unfortunately, we are not able to sponsor visas, including CPT/OPT or employ corp-to-corp.
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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, Ernst & Young, 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.
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.
Skills Required
- Strong machine learning knowledge, either in final years of ML PhD or graduated
- Strong writing and verbal communication skills
- Action-oriented mindset balancing creative problem-solving and results delivery
- Analytical, planning, and process improvement capability
- Experience in a fast-paced entrepreneurial environment
- Familiarity with Python, GPU, AWS, API, LLM, ML, and SQL
Scale AI Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Scale AI and has not been reviewed or approved by Scale AI.
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Healthcare Strength — Healthcare coverage is described as comprehensive across medical, dental, and vision, with flexibility to choose plans that fit individual or family needs. A monthly wellness stipend further supports physical and mental wellbeing expenses.
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Equity Value & Accessibility — Equity-based compensation is included in eligible packages, positioning ownership as a meaningful component of total rewards for many full-time roles. An employee stock purchase plan also provides an additional pathway to participate in potential upside.
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Leave & Time Off Breadth — Paid time off is positioned as generous with a flexible policy intended to support recharging and burnout prevention. Paid holidays and paid sick days are also part of the time-off offering.
Scale AI Insights
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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