Senior Machine Learning Engineer
K2 PARTNERING SOLUTIONS PTE. LTD. · Singapore ·
- Seniority
- Senior
- Category
- ML ai
Build and scale core ML infrastructure and systems, turning research models into production-ready services while ensuring reliability and performance for user-facing AI features.
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.
TechnologiesPython, 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.