Liquid AIMember of Technical Staff - Machine Learning Research Engineer, Post-Training
Seeking a Post-Training ML Researcher to refine, evaluate, and improve foundation models using post-training techniques and data engineering.
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.