Machine Learning Evaluation Specialist designing tasks for unsolved ML problems using deep domain expertise. Opportunity for graduates to contribute to challenging AI research tasks on a fully remote basis.
About the role
- Software Engineer, Machine Learning developing next-generation AI technologies for clinical trial platforms. Building scalable data pipelines, optimizing model performance, and collaborating with cross-functional teams in a hybrid setting.
Responsibilities
- Lead the development of next-generation AI technologies that power clinical trial platforms.
- Build scalable data pipelines for AI.
- Optimize model performance on hardware accelerators.
- Deploy state-of-the-art LLMs and Multi-Modal models.
- Collaborate with clinical operations, product, and research teams to translate complex business requirements into robust engineering solutions.
- Mentor engineers across the organization, fostering best practices in distributed systems, ML engineering, and the adoption of AI-native development workflows.
Requirements
- Bachelor's degree in computer science, Engineering, or a related technical field, or equivalent practical experience.
- 8+ years of experience in software development with proficiency in Python, C++, or similar languages.
- 7+ years of experience leading technical project strategy and optimizing industry-scale ML infrastructure (model deployment, evaluation, fine-tuning).
- 2+ years of experience with state-of-the-art AI techniques (LLMs, RAG, Computer Vision) and frameworks (PyTorch, TensorFlow, JAX).
- Demonstrated proficiency in using AI models to augment software engineering practices—specifically using LLMs for code generation, debugging, architectural design validation, and documentation.
- Experience designing and scaling data infrastructure, including distributed storage systems, data lakes, or high-performance ETL frameworks.
Benefits
- Equal opportunity workplace
- Fast-paced environment
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