Skip to contentSenior ML Researcher – Applied Machine Learning
| Company | Red Cell Partners |
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| Location | Seattle, WA, USA |
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| Salary | $175000 – $225000 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Senior, Expert or higher |
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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.
- Educational Background: A Master’s or PhD in Computer Science, Machine Learning, or a related field, with a focus on ML research.
- 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.
- Cross-Functional Collaboration: Work closely with product teams and domain experts to translate business needs into research questions and actionable ML strategies.
- 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.
Preferred Qualifications
No preferred qualifications provided.
