Trase Systems is looking to hire a Senior Machine Learning Engineer to advance their ML systems, focusing on model training, pipeline development, and fine-tuning of large language models (LLMs) to ensure peak performance and align with evolving business needs.
Requirements
- ML Systems Expertise: Proven experience in developing, optimizing, and deploying ML systems in production environments.
- Model Training and Pipeline Mastery: Strong background in building and managing end-to-end training pipelines for ML models.
- LLM Fine-Tuning: Extensive knowledge and hands-on experience in fine-tuning large language models for specific use cases and optimizing them for targeted outcomes.
- Framework Proficiency: Skilled in ML frameworks such as TensorFlow, PyTorch, or similar tools used in ML model development.
- Programming Skills: Proficient in Python with a focus on writing efficient, clean, and maintainable code for ML applications.
- Impactful ML Solutions: A track record of delivering and implementing machine learning solutions that have successfully driven value in real-world applications.
Responsibilities
- Architect, Build, and Optimize ML Systems: Develop and deploy robust ML models that deliver high-impact results for real-world applications.
- Training Pipeline Development: Design and implement efficient, scalable pipelines to train and retrain ML models, ensuring they meet business needs.
- Fine-Tuning Large Language Models (LLMs): Continuously fine-tune LLMs to align with specific enterprise requirements, enhancing accuracy, relevance, and performance.
- Feedback Systems Design: Implement and refine feedback loops to iteratively improve the effectiveness of ML models over time.
- Stay Current with ML Advancements: Keep up with the latest in ML research and best practices, applying insights to our ML infrastructure to ensure it remains at the cutting edge.
- Mentorship and Knowledge Sharing: Guide and mentor junior team members, fostering a culture of continuous improvement and technical growth.
- Technical Communication: Clearly and effectively communicate ML methodologies, results, and insights to non-technical stakeholders.
Other
- Cross-Functional Collaboration: Work closely with product and business teams to understand and translate requirements into ML solutions that provide tangible outcomes.
- Clear Communicator: Ability to distill complex ML concepts for both technical and non-technical audiences.
- Some travel is required
- Competitive salary and performance-based bonuses.
- Opportunities for professional development and continued learning.
