Principal ML Engineer

Nicoll Curtin; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Nicoll Curtin is partnering with a tech company to hire a Principal Machine Learning Engineer to build and improve the core ML systems behind its consumer applications, spanning LLMs, agents, long-running workflows, persistent context and memory, tool use, inference and evaluation.

This is a hands-on individual contributor role with technical leadership responsibility, working closely with Research and Application Engineering to take complex ML problems from experimentation into production.

Qualifications

• Excellent Python skills and ML fundamentals.
• Experience with PyTorch or JAX in production ML.
• Own production ML systems from deployment to operation.
• Ability to solve complex, ambiguous ML problems and ship measurable improvements.
• Strong understanding of model evaluation and production trade-offs.
• Robust software engineering practices and cross-team collaboration.

Responsibilities

• Experimentation and evaluation through deployment and ongoing improvement.
• Build reliable model and agent capabilities for real products.
• Develop evaluations to measure quality and identify failures.
• Improve production performance in latency, reliability, safety and cost.
• Apply training, fine tuning or inference improvements where needed.
• Set technical direction, mentor engineers and stay close to implementation.

Skills

Python

ML fundamentals

PyTorch

JAX

production ML

problem solving

model evaluation

software engineering

Tools

PyTorch

JAX

Job description

Nicoll Curtin is partnering with a tech company to look for a Principal Machine Learning Engineer to build and improve the core ML systems behind its consumer applications, spanning LLMs, agents, long running workflows, persistent context and memory, tool use, inference and evaluation.

This is a hands on individual contributor role with technical leadership responsibility, working closely with Research and Application Engineering to take complex ML problems from experimentation into production.

What you will do

• Experimentation and evaluation through deployment and ongoing improvement
• Build reliable model and agent capabilities that support real product experiences
• Develop evaluations to understand model behaviour, measure quality and identify failures
• Improve production performance across latency, reliability, safety and cost
• Apply training, fine tuning or inference improvements where they solve the problem
• Set technical direction within your area, mentor engineers and stay close to implementation

What we are looking for

• Excellent Python skills, strong ML fundamentals and practical experience with PyTorch or JAX
• Personal ownership of production ML systems, including implementation, deployment and operation
• Ability to independently solve complex, ambiguous ML problems and ship measurable improvements
• Strong understanding of evaluation, model behaviour and production trade offs
• Robust software engineering practices and the ability to collaborate across research and product engineering

Experience that would strengthen your application

• LLM or agent systems, persistent memory, tool use or long running workflows
• Model training, fine tuning, alignment or distillation for real products
• Scalable inference, distributed training or serving, quantisation or GPU optimisation
• Evaluation systems that improve model quality, robustness or safety

Why consider this role

• Own meaningful technical problems: Take complex ML systems from experimentation into production, with ownership of technical decisions and measurable outcomes.
• Build AI capabilities for real users: Work on consumer applications involving LLMs, agents, persistent memory, tool use and long running workflows.
• Shape technical direction while staying hands on:Influence architecture and engineering standards, mentor others and remain close to implementation.
• Work closely with Research and Engineering: Bridge model development and product delivery, seeing how your decisions affect quality, reliability and the user experience.
• Bring depth in your strongest area: The team values expertise in inference, evaluation or applied ML or training, without expecting equal depth across every specialism.
• Be assessed on what you have built: Principal level is defined by technical depth, independent ownership and influence, rather than previous title or years of experience alone.

We welcome engineers with particular depth in inference, evaluation or applied ML, as well as those specialising in training and fine tuning.

(Tip for a successful consideration: As you prepare your application, we encourage you to carefully review the requirements associated with this role to ensure eligibility. To support a meaningful assessment of your fit, your resume should provide clear, detailed examples of your contributions, measurable impact, and relevant commercial experience that align with the role’s criteria. Please be aware that we rely solely on the information presented in your application - if specific experience or achievements are not included, we are unable to infer or assume them.)

Lastly, we will be only be able to consider candidates who are currently based in Singapore.