Applied AI Scientist, Small Language Model and AI Training

<div class="content-intro"><h2><strong>Who Are We?</strong></h2> <p>Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster.</p> <p>The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman.</p> <p>P.S: We highly recommend reading <a href="https://api-first-world.com/">The "API-First World" graphic novel</a> to understand the bigger picture and our vision at Postman.</p></div><h2><strong>The Opportunity</strong></h2> <p data-renderer-start-pos="5258">As an Applied Scientist specializing in Small Language Models and AI Training, you will lead research and development efforts focused on building efficient, high-performance language models tailored for practical applications. You will work closely with research, engineering, and product teams to advance model training techniques, optimize architectures, and scale AI solutions. Your work will directly contribute to AI systems that are safe, interpretable, and impactful across diverse usage scenarios.</p> <h2><strong>What You’ll Do</strong></h2> <ul data-indent-level="1"> <li> <p data-renderer-start-pos="5785">Lead research and development of novel training methodologies and architectures for small and efficient language models.</p> </li> <li> <p data-renderer-start-pos="5909">Design, implement, and evaluate model training experiments to improve performance, robustness, and generalization of language models.</p> </li> <li> <p data-renderer-start-pos="6046">Collaborate closely with research scientists and engineers on scalable training pipelines and model deployment strategies.</p> </li> <li> <p data-renderer-start-pos="6172">Develop techniques for model compression, fine-tuning, and domain adaptation to optimize models for real-world applications.</p> </li> <li> <p data-renderer-start-pos="6300">Ensure AI safety, fairness, and alignment principles are integrated into model training processes and evaluated rigorously.</p> </li> <li> <p data-renderer-start-pos="6427">Mentor and support cross-functional teams on applied machine learning methods and best practices.</p> </li> <li> <p data-renderer-start-pos="6528">Evaluate and integrate new tools, frameworks, and datasets to accelerate AI training workflows.</p> </li> <li> <p data-renderer-start-pos="6627">Partner with product teams to translate model capabilities into actionable features aligned with user needs and ethical standards.</p> </li> </ul> <h2><strong>About You</strong></h2> <ul data-indent-level="1"> <li> <p data-renderer-start-pos="6793">Have demonstrated experience in applied research or engineering roles focused on training language models, ideally small or efficient models.</p> </li> <li> <p data-renderer-start-pos="6938">Strong programming skills in Python and familiarity with machine learning frameworks such as PyTorch, TensorFlow, or <span data-highlighted="true" data-vc="highlighted-text"><span data-testid="definition-highlighter">JAX</span></span>.</p> </li> <li> <p data-renderer-start-pos="7063">Deep understanding of language model architectures, training techniques, and optimization strategies.</p> </li> <li> <p data-renderer-start-pos="7168">Experience with distributed training, data pipeline design, and scalable AI infrastructure.</p> </li> <li> <p data-renderer-start-pos="7263">Passion for AI safety, interpretability, and delivering user-centered AI technology.</p> </li> <li> <p data-renderer-start-pos="7351">Excellent communica ... (truncated, view full listing at source)