We’re seeking an experienced Machine Learning/AI Engineer who is passionate about bridging innovative research with production-level deployment. In this role, you will work closely with researchers, data teams, and platform engineers to transform innovative ideas into scalable, efficient, and robust production systems. You’ll have the opportunity to develop and refine ML workflows and drive best practices across the entire ML lifecycle—from initial prototyping to inference and support.

You will take on the following responsibilities:

• Collaborate with ML researchers to explore model architectures, training methodologies, and experimental objectives
• Translate prototypes into reusable, maintainable, and production-ready code, ensuring research insights scale seamlessly into operation
• Design, implement, and optimize fully automated training pipelines and evaluation workflows
• Optimize models for production—improving efficiency through techniques like quantization, distillation, and intelligent batching
• Continuously assess and fine-tune both research and production workflows, integrating user feedback and automated monitoring systems
• Identify and implement opportunities for process improvements to reduce bottlenecks and improve overall modeler efficiency.
• Stay ahead of industry trends and new technologies, incorporating sophisticated methodologies into production systems
• Provide technical guidance and mentorship to peers, ensuring the team uses new tools and techniques that drive innovation

You should possess the following qualifications:

• BS in Computer Science, Mathematics, Physics, or related technical subject area
• Minimum 1 year of experience required; 7-15 years of experience preferred
• Strong software engineering skills using Python, version control, testing frameworks, and CI/CD practices
• Ability to work across functions and influence technical strategy while balancing research innovation with operational excellence
• A phenomenal teammate that can work efficiently with modeling and engineering partners
• Experience with leading ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace) and familiarity with model serving/deployment tools, distributed systems and data infrastructures
• Experience with the following are preferred but not required:
  • Working with LLMs, multimodal models, or time-series forecasting
  • ML Ops platforms
  • Background in research or a proven understanding of ML theory