Trase Systems aims to empower enterprise leaders to harness the full potential of AI without the associated complexity and risks by providing an end-to-end solution for deploying, managing, and optimizing AI. The company specializes in bridging the “last mile” of AI adoption, unlocking AI's full potential while driving efficiency and significant cost savings.
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
- Active Secret or Top Secret Clearance
- 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.
- Educational Background: Bachelor’s or Master’s degree in Machine Learning, Computer Science, Data Engineering, or a related field.
- Location: McLean, VA
