Trase Systems aims to solve the complexity and risks associated with deploying, managing, and optimizing AI in the enterprise, focusing on bridging the "last mile" of AI adoption to unlock its full potential and drive efficiency and cost savings.
Requirements
- Expertise in ML Model Training and Optimization: Proven experience with ML research, including designing and evaluating novel training methodologies, model architectures, and optimization techniques.
- Deep Knowledge of Language Model Fine-Tuning: Demonstrated proficiency in customizing and fine-tuning language models to meet specific use cases, with experience in models such as GPT, BERT, or similar frameworks.
- Proficiency in ML Frameworks: Strong understanding of machine learning and NLP frameworks like TensorFlow, PyTorch, or similar, with the ability to design and implement custom model architectures.
- Programming Skills: Proficiency in Python with an emphasis on writing efficient, maintainable, and scalable code.
- Research Communication Skills: Ability to present complex technical concepts to both technical and non-technical stakeholders, highlighting the business impact of ML innovations.
- Impactful ML Solution Delivery: Proven track record of delivering ML solutions that have made significant real-world impact, ideally within an enterprise or production setting.
Responsibilities
- Lead ML Research and Development: Drive the research, development, and optimization of machine learning models, focusing on solving real-world business problems through advanced ML techniques.
- Architect Novel Training and Fine-Tuning Methodologies: Design, implement, and iterate on advanced training protocols, fine-tuning processes, and optimization strategies, particularly for Language Models (LLMs).
- Evaluate Model Performance and Innovation: Develop and refine techniques for assessing and enhancing the effectiveness of ML models, focusing on accuracy, scalability, and adaptability to dynamic enterprise requirements.
- Feedback System Design for Continuous Learning: Create systems that incorporate user and system feedback to iteratively improve model performance over time.
- Stay Current on ML Advancements: Actively monitor the latest research in ML and NLP, integrating cutting-edge practices and methodologies into our development pipeline.
- Mentor and Guide Team Members: Provide technical guidance to junior researchers, fostering a culture of continuous learning, experimentation, and research-driven development.
Other
- Cross-Functional Collaboration: Work closely with product teams and domain experts to translate business needs into research questions and actionable ML strategies.
- Educational Background: A Master’s or PhD in Computer Science, Machine Learning, or a related field, with a focus on ML research.
- Some travel is required.
- Competitive salary and performance-based bonuses.
- Comprehensive health and wellness benefits package.
