Machine Learning Engineer

About ElastixAI

ElastixAI is an early-stage startup aiming to revolutionize AI inference infrastructure. We are developing a cutting-edge solution that significantly enhances efficiency. Our platform dynamically adapts to various deployments, continuously evolving to support future AI use cases.

Role Summary

We are seeking a talented Machine Learning Engineer to contribute to the development of our core AI inference platform. This role involves designing and developing critical components, optimizing inference performance, collaborating with systems and cloud engineers, and contributing to API and tool design.

Key Responsibilities

• Design, develop, and maintain core components of our ML platform, ensuring scalability, reliability, and ease of use.
• Research, prototype, and implement advanced ML techniques to optimize inference performance across diverse hardware targets.
• Collaborate with systems and cloud engineers to efficiently utilize underlying hardware resources.
• Contribute to the design of APIs and tools that facilitate seamless integration and management of our inference solutions.

Required Qualifications

• PhD/MS in Computer Science, Computer Engineering, or a related field with 3+ years of machine learning R&D and deployment experience.
• Strong proficiency in one or more programming languages such as Python or C++.
• Proficiency in at least one ML framework (e.g., PyTorch, TensorFlow, JAX).
• Solid understanding of software engineering best practices, including data structures, algorithms, and testing.
• Excellent problem-solving abilities and a strong aptitude for tackling complex technical challenges.
• Strong communication skills and a proven ability to collaborate effectively in a cross-functional team environment.
• Ability to thrive in a fast-paced, dynamic startup environment.

Preferred/Bonus Qualifications

• Experience with training generative machine learning models.
• Experience with optimizing the performance of machine learning models.
• Experience leading research initiatives in Machine Learning, Natural Language Processing, and related fields.
• Familiarity with performance analysis, profiling, and optimization techniques.
• Experience with cloud platforms (AWS, GCP, Azure).
• Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).