Senior Machine Learning Engineer

K2 Partnering Solutions; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

K2 Partnering Solutions is seeking a Senior Member of Technical Staff – Machine Learning to drive the development of our core ML infrastructure. You will lead end-to-end execution from translating ambiguous requirements into practical designs to shipping scalable systems into production.

This is a hands-on role demanding deep technical expertise and systems thinking. You will architect, deploy, and maintain core ML systems powering long-horizon AI features, oversee data pipelines, training,

Qualifications

• Proven ability to design scalable ML systems and architectures.
• Excellent cross-functional collaboration and communication skills.
• Capability to lead and mentor engineers across teams.
• Self-driven with an iterative, result-oriented mindset.

Responsibilities

• Architect, deploy, and maintain core ML systems powering long-horizon AI features.
• Manage the full ML lifecycle: data pipelines, model training, evaluation, inference, CD.
• Translate research models into production-ready microservices.
• Monitor and resolve production issues with latency, cost, and safety in mind.
• Collaborate with Research, Product, and Platform teams to deliver user value.
• Provide technical guidance and mentorship to junior and mid-level ML engineers.

Skills

System design

Cross-functional leadership

Strong communication

Self-directed problem solving

Tools

Python

PyTorch

JAX

Distributed GPU Training & Inference Workflows

Job description

We are seeking a Senior Member of Technical Staff – Machine Learning to drive the development of our core ML infrastructure and subsystems. In this role, you will lead end-to-end execution—from translating ambiguous requirements into practical designs to shipping scalable systems into production. This is a hands-on position requiring deep technical expertise and strong systems-level thinking.

Key Responsibilities

• Architect, deploy, and maintain core ML systems powering long-horizon AI features.
• Manage the full ML lifecycle, including data pipelines, model training, evaluation, inference, and continuous deployment.
• Convert experimental research models into resilient, production-ready microservices.
• Monitor, debug, and resolve complex production anomalies within strict latency, cost, and safety constraints.
• Work cross-functionally with Research, Product, and Platform teams to deliver user-facing value.
• Provide technical guidance, architectural oversight, and mentorship to junior and mid-level ML engineers.

Technologies:

Python, PyTorch / JAX, Distributed GPU Training & Inference Workflows.

Qualifications

• Demonstrated experience shipping and sustaining production ML systems with active user bases.
• Strong mastery of production software engineering practices (modular design, testing, maintainability).
• Deep familiarity with modern deep learning models, optimization techniques, and edge-case behavior.
• Self-directed problem solver with excellent communication skills and an iterative approach to development.

Key Performance Indicators (KPIs)

• System Reliability: Production ML services consistently meet or exceed performance, latency, and reliability benchmarks.
• Operational Excellence: Fast resolution of production issues with minimal impact on service availability.
• Business Alignment: ML initiatives deliver measurable improvements to core product metrics and business objectives.
• Engineering Quality: Raised team standards through rigorous code reviews and impactful mentorship.