Senior ML Engineer

Summary

Join Trase Systems, a company empowering enterprise leaders to harness AI's full potential, as a Senior Machine Learning Engineer. You will play a key role in advancing our ML systems, focusing on model training, pipeline development, and fine-tuning large language models (LLMs). Responsibilities include architecting, building, and optimizing ML systems; designing and implementing efficient training pipelines; fine-tuning LLMs for specific enterprise requirements; implementing feedback loops; collaborating with product and business teams; staying current with ML advancements; mentoring junior team members; and communicating effectively with both technical and non-technical stakeholders. This fully remote position offers a competitive salary and benefits package. The ideal candidate possesses extensive experience in developing, optimizing, and deploying ML systems, a strong background in building and managing training pipelines, and proven expertise in fine-tuning LLMs. A Bachelor's or Master's degree in a related field is required.

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
• Clear Communicator: Ability to distill complex ML concepts for both technical and non-technical audiences
• Educational Background: Bachelor’s or Master’s degree in Machine Learning, Computer Science, Data Engineering, or a related field
• 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
• Cross-Functional Collaboration: Work closely with product and business teams to understand and translate requirements into ML solutions that provide tangible outcomes
• 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

Benefits

• Competitive salary and performance-based bonuses
• Comprehensive health and wellness benefits package
• Flexible work hours
• Opportunities for professional development and continued learning
• Collaborative and inclusive work environment