Principal Machine Learning Engineer

About the Role

Nicoll Curtin is collaborating with a technology company to recruit a Principal Machine Learning Engineer. This role entails architecting and enhancing foundational ML systems that power consumer-facing applications, including work with large language models, agents, long-duration workflows, persistent context and memory, tool utilization, inference, and performance evaluation.

Key Responsibilities

• Conduct experimentation and continual evaluation from testing phases through to deployment and ongoing refinement.
• Develop dependable model and agent functionalities integral to authentic product experiences.
• Create evaluation frameworks to analyze model behavior, assess quality, and detect failures.
• Enhance production-level performance focusing on latency, reliability, safety, and cost-efficiency.
• Implement training, fine-tuning, or inference enhancements whenever applicable to address challenges.
• Define technical strategy within your domain, mentor peers, and maintain close involvement in implementation.

Candidate Profile

• Solid proficiency in Python programming, with a robust understanding of machine learning principles and practical experience using PyTorch or JAX.
• Hands-on responsibility for production ML systems covering end-to-end implementation, deployment, and maintenance.
• Capable of independently resolving complex and unclear ML challenges while delivering quantifiable improvements.
• Strong grasp of model evaluation methodologies, model behavior insight, and trade-offs in production environments.
• Demonstrated stringent software engineering disciplines and collaboration skills across research and product engineering teams.

Desirable Experience

• Working knowledge of large language model or agent-based systems, persistent memory integration, tool operation, or prolonged workflow systems.
• Expertise in model training, fine-tuning, alignment, or distillation specifically aimed at real-world product implementations.
• Familiarity with scalable inference engines, distributed training or serving, quantization, and GPU performance tuning.
• Creation and refinement of evaluation frameworks aimed at boosting model robustness, quality, or safety.

Why Join This Team?

• Take ownership of technically complex ML problems, leading them from research-stage experimentation to production deployment with direct responsibility for outcomes.
• Develop AI capabilities that directly affect end users, working on innovative consumer applications leveraging LLMs, agents, persistent memory, tool use, and ongoing workflows.
• Influence the technical roadmap and standards, mentor fellow engineers, all while staying deeply involved with hands-on implementation.
• Collaborate closely with both Research and Application Engineering teams to bridge model innovation with product delivery, directly impacting user experience quality and reliability.
• Bring specialized expertise in areas like inference, evaluation, applied machine learning, or training without the expectation to master every subdomain.
• Evaluation criteria emphasize what you have built and your independent impact rather than solely previous titles or years in the field.

Additional Information

Applications will only be accepted from candidates currently residing in Singapore. Applicants are encouraged to submit detailed resumes containing specific examples of contributions, measurable impacts, and relevant commercial experience related to the job criteria. Applications lacking substantive information on experience or achievement will not be inferred or assumed to meet requirements.