Summary
What you’ll impact
Our company is seeking a Machine Learning Engineer to lead the design, development, and deployment of advanced ML models that improve system performance and scalability, while translating research into production‑ready, adversarial‑resistant AI systems. The role bridges applied research and engineering, requiring expertise in deep learning, distributed systems, and large‑scale model monitoring.
Responsibilities
What you'll do
- Lead the design, development, and deployment of advanced machine learning models to enhance system performance and scalability.
- Tackle complex challenges associated with resource-intensive models using distributed systems and parallel computing.
- Advance methodologies for controlling, monitoring, and analyzing machine learning models in production environments.
- Develop new approaches to adversarial testing, model evaluation, and robust inference.
- Translate research ideas into scalable AI systems deployed in real-world, adversarial settings.
- Work closely with cross-functional teams to ensure research outcomes inform production systems.
Requirements
What you’ll bring
- Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field is required.
- Experience in building and deploying machine learning models and systems.
- Demonstrated expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
- Practical experience developing scalable machine learning pipelines and integrating them with cloud infrastructure (e.g., AWS, GCP, Azure).
- Experience conducting ML research, including building research prototype systems, experiment design, empirical analysis of results, and communicating results via publications.
- In-depth knowledge of neural network architectures, including sequence models, transformers, and other state-of-the-art approaches.
- Strong algorithmic problem-solving skills and comprehensive knowledge of ML theory and optimization techniques.
- Proficiency in data preprocessing, transformation, and handling large-scale, multi-modal datasets.