Machine Learning Engineer, Vulcan

<p><strong><span data-contrast="none">About the role</span></strong><span data-ccp-props="{"134233117":false,"134233118":false,"335559738":240,"335559739":240}"> </span></p> <p>We are looking for a talented Machine Learning Engineer to join our Product Core Engineering team. You will be responsible for building and optimizing machine learning workflows that directly power our AI-driven products. This role focuses on the full lifecycle of model development — from training and fine-tuning to deployment and monitoring — ensuring robust and efficient ML systems at scale.</p> <h4><strong>Why Join Us?</strong></h4> <ul> <li>Product Impact: Your work will be directly embedded in our core AI products, shaping user experience and product capabilities.</li> <li>Engineering Excellence: Be part of a team that values high-quality engineering, reproducibility, and scalability.</li> <li>Innovation: Opportunity to experiment with cutting-edge ML and GenAI technologies in production settings.</li> <li>Collaboration: Work alongside backend, platform, and product teams in a highly collaborative environment.</li> <li>Competitive Package: Receive attractive compensation and benefits aligned with your skills and performance.</li> </ul> <h4><strong>Key Responsibilities</strong></h4> <ul> <li>Model Development: Design and implement training processes for machine learning classifiers and generative models.</li> <li>Fine-tuning & Prompting: Adapt pre-trained models to specific product needs through fine-tuning, prompt engineering, and parameter optimization.</li> <li>Hyperparameter Management: Configure and tune hyperparameters to balance accuracy, robustness, and performance.</li> <li>Pipeline Engineering: Build scalable training and evaluation pipelines to support continuous experimentation.</li> <li>Integration: Collaborate with backend and product engineers to deploy models into production systems.</li> <li>Monitoring & Maintenance: Establish monitoring metrics and retraining strategies to maintain model performance in dynamic environments.</li> </ul> <p><strong><span data-ccp-props="{"201341983":0,"335557856":16777215,"335559739":240,"335559740":240}">Qualifications</span></strong></p> <ul> <li>Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.</li> <li>Proven experience in training classifiers and fine-tuning transformer-based models.</li> <li>Strong understanding of tokenization techniques, embedding models, and vector representations.</li> <li>Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.</li> <li>Familiarity with hyperparameter tuning and model configuration best practices.</li> <li>Experience working with large-scale datasets and building reproducible ML pipelines.</li> <li>Fluent in Mandarin; proficiency in English is an advantage.</li> </ul> <h4><strong>Desired Skills</strong></h4> <ul> <li>Knowledge of cloud platforms (AWS, Azure, GCP) and containerized deployment (Docker, Kubernetes).</li> <li>Experience with LLM prompting and fine-tuning is a plus.</li> <li>Basic understanding of data engineering practices (ETL, data validation, feature engineering).</li> <li>Exposure to AI safety, security, or bias mitigation techniques is an advantage.</li> </ul> <p> </p> <h3><strong><span data-contrast="auto">Other Benefits</span></strong><span data-ccp-props="{"201341983":0,"335559739":80,"335559740":259}">