MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Senior Machine Learning Engineer enhancing real-time AI safety solutions at Gray Swan AI. Lead development of scalable machine learning models and ensure robustness against adversarial threats.
Responsibilities
- 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.
- Mentor junior engineers and drive innovation within the team.
- Work closely with cross-functional teams to ensure research outcomes inform production systems.
Requirements
- Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field is required.
- A Master’s or PhD in a relevant technical field is strongly preferred, especially with a focus on machine learning and AI safety.
- 6+ years of hands-on experience in building and deploying machine learning models and systems.
- Experience with modern ML methods such as LLMs (training, finetuning, and/or analyzing), synthetic data generation pipelines, and AI safety or security work.
- Demonstrated expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
- 6+ years of experience programming in Python and C++ (preferred).
- 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.
Benefits
- 401k with up to 4% matching
- 28 days annual leave (vacation + holidays)
- Health, dental, and vision coverage
- Catered lunches (Pittsburgh office)
- Flexible work arrangements
- Visa sponsorship available for exceptional candidates
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