Member of Technical Staff - Machine Learning Research Engineer, Post-Training

Liquid AI; San Francisco (CA); On-site; USD 60,000 - 240,000; Full time

Job summary

Liquid AI, an MIT spin-off, seeks a Member of Technical Staff specializing in Machine Learning Research to enhance their foundation models. This hands-on role involves refining AI capabilities through innovative techniques, working closely with cross-functional teams and targeting specific use-case requirements.

Qualifications

• Hands-on experience in model fine-tuning.
• Expertise in generating and curating high-quality post-training data.
• Strong understanding of technical challenges in post-training optimization.

Responsibilities

• Fine-tune both general-purpose and specialized models.
• Design post-training datasets and develop evaluation frameworks.
• Participate in post-training research discussions.

Skills

Model fine-tuning

High-quality data engineering

Reinforcement learning techniques

Post-training optimization

Job description

Member of Technical Staff - Machine Learning Research Engineer, Post-Training

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Member of Technical Staff - Machine Learning Research Engineer, Post-Training

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Liquid AI, an MIT spin-off, is a foundation model company headquartered in Boston, Massachusetts. Our mission is to build capable and efficient general-purpose AI systems at every scale.

Our goal at Liquid is to build the most capable AI systems to solve problems at every scale, such that users can build, access, and control their AI solutions. This is to ensure that AI will get meaningfully, reliably and efficiently integrated at all enterprises. Long term, Liquid will create and deploy frontier-AI-powered solutions that are available to everyone.

We are seeking a Post-Training ML Researcher to join our team. This is a hands-on, technical role that will focus on refining, evaluating, and improving our foundation models for both general-purpose and specialized use cases. Your work will primarily center on enhancing model capabilities through post-training techniques and high-quality data engineering.

What You'll Actually Do

• Fine-tune both general-purpose and specialized models to align with specific use-case requirements
• Design post-training datasets and develop rigorous evaluation frameworks to target and quantify model improvements
• Participate in post-training research discussions and collaborate with cross-functional teams to translate novel ideas into practice
  
  
  
  

What Makes You Stand Out

• Hands-on experience in model fine-tuning, particularly with direct alignment and reinforcement learning techniques
• Expertise in generating, curating, and evaluating high-quality post-training data to effectively target model capabilities and limitations
• Strong understanding of technical challenges in post-training optimization, including hallucination mitigation, reasoning improvement, and safety guardrail enhancement
• Proven ability to publish research, models, or novel techniques in post-training optimization
• Experience in developing both general-purpose and specialized domain models
• Successful track record of building automated pipelines for continuous model evaluation and improvement
  
  
  
  

What You'll Get

• Advance the state-of-the-art in model post-training techniques
• Gain experience working on cutting-edge AI systems that push the boundaries of LFM performance
• Become an expert in the entire post-training lifecycle from data generation to evaluation
  
  

Seniority level

• Seniority level

  Not Applicable

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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