Member of Technical Staff, Model Training (post-training research)

About the Role

The Role In this role, you will investigate how learning algorithms, training data, and evaluation methodologies interact to produce stronger reasoning, generalization, and agent capabilities. You will explore new approaches to reinforcement learning, distillation, and data-efficient training, translating research insights into scalable methods that improve model intelligence. This is a highly experimental role at the intersection of fundamental machine learning research and large-scale model development. You will have the opportunity to formulate research hypotheses, design ambitious experiments, and develop training methods that directly influence the capabilities of our models. Nice to Have • Experience with RLHF, RLAIF, preference optimization, or reinforcement learning for reasoning and agentic tasks. • Familiarity with distributed training, large-scale experimentation, and GPU-based model optimization. • Experience developing reward models, verifiers, graders, or automated evaluation systems. • Research contributions to post-training, reinforcement learning, or related areas through publications, open-source projects, or production systems. • Experience investigating synthetic data generation, curriculum learning, or iterative self-improvement techniques. Compensation & Benefits Notice The national pay range for this full-time position is base salary of $200,000 –$320,000. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce. micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to support@micro1.ai. Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

Responsibilities & Tasks

• Research, implement, and scale advanced post-training methods, including supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD), across large language and multimodal models.
• Investigate learning objectives, optimization dynamics, exploration strategies, and credit assignment mechanisms to improve training stability, sample efficiency, and capability acquisition.
• Develop and refine training recipes by jointly optimizing data mixtures, sampling strategies, curricula, and evaluation methodologies.
• Design methods for learning from expert demonstrations, human preferences, corrections, and model-generated trajectories, identifying where human feedback delivers the greatest improvements.
• Research the co-evolution of generators, reward models, and graders, developing approaches that improve both model performance and the quality of the training signals they consume.
• Design rigorous experiments, ablation studies, and evaluation frameworks to measure generalization, reasoning, and performance on realistic downstream tasks.
• Own research initiatives end-to-end, from identifying promising directions and implementing new algorithms to executing large-scale experiments and validating results.

Qualifications & Requirements

• Strong foundations in machine learning, deep learning, optimization, and modern model training techniques.
• Hands-on experience training, fine-tuning, or post-training large-scale language or multimodal models.
• Deep technical expertise in one or more areas such as reinforcement learning, model distillation, reward modeling, data selection, or learning dynamics.
• Strong proficiency in Python and PyTorch, with the ability to implement, debug, and optimize complex training pipelines.
• Experience designing controlled experiments, interpreting model behavior, and translating empirical findings into actionable research directions.
• Ability to independently navigate ambiguous research problems, formulate hypotheses, and iterate quickly based on experimental evidence.
• Strong analytical thinking and a research-driven mindset, with an emphasis on reproducibility, scientific rigor, and measurable improvements.