Machine Learning Research Scientist / Research Engineer, Post-Training

Scale AI, Inc.; Seattle (WA); On-site; USD 220,000 - 325,000; Full time

Job summary

An innovative company is seeking Research Scientists and Engineers to advance GenAI research. This role focuses on optimizing LLM capabilities through post-training techniques like SFT and RLHF. You will collaborate with top AI labs, develop methods to enhance model alignment, and publish findings at prestigious conferences. Join a forward-thinking team dedicated to accelerating AI adoption across industries, offering a competitive salary and comprehensive benefits. If you're passionate about AI and eager to make an impact, this opportunity is for you.

Benefits

Health Coverage

Retirement Plans

Paid Time Off

Equity Options

Stipends

Qualifications

• Ph.D. or Master's in Computer Science, Machine Learning, or related fields.
• Deep understanding of deep learning and reinforcement learning.

Responsibilities

• Develop novel methods to enhance alignment of large-scale generative models.
• Research and develop post-training techniques like SFT and RLHF.

Skills

Reinforcement Learning

Deep Learning

Large-Scale Model Fine-Tuning

Communication Skills

Bias Reduction Techniques

Education

Ph.D. in Computer Science

Master's in Machine Learning

Job description

Scale collaborates with leading AI labs to advance GenAI research by providing high-quality data. We seek Research Scientists and Research Engineers specializing in LLM post-training techniques such as SFT, RLHF, and reward modeling. The role emphasizes optimizing data curation and evaluation to improve LLM capabilities across text and multimodal modalities.

Responsibilities include developing novel methods to enhance the alignment and generalization of large-scale generative models, collaborating with researchers and engineers to establish best practices, and partnering with top foundation model labs to contribute technical and strategic insights into next-generation AI models.

You will:

• Research and develop post-training techniques like SFT, RLHF, and reward modeling to improve LLM capabilities.
• Design and test new preference optimization approaches.
• Analyze model behavior, identify weaknesses, and propose solutions for bias reduction and robustness.
• Publish findings at top-tier AI conferences.

Ideally you'd have:

• A Ph.D. or Master's in Computer Science, Machine Learning, AI, or related fields.
• Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.
• Experience with post-training methods such as RLHF and instruction tuning.
• Excellent communication skills.
• Published research in major ML conferences or journals.

Compensation includes salary, equity, and benefits, with a range of $220,000 to $325,000 USD depending on location and experience. Benefits encompass health coverage, retirement plans, stipends, PTO, and potential additional perks.

Note: There is a 90-day waiting period before reconsidering candidates for the same role.

About Us:

At Scale, we aim to accelerate AI adoption across industries, powering advanced models trusted by leading companies and government agencies. We value diversity and are committed to equal opportunity employment and reasonable accommodations for applicants with disabilities.