Machine Learning Engineer

TECHKNOWLEDGEY PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

TECHKNOWLEDGEY PTE. LTD. is seeking a Member of Technical Staff, Machine Learning, to build core ML components for production from day one. You will ship, debug, and iterate on real-world ML systems under production constraints.

You will work with data pipelines, training loops, and evaluation, collaborating with senior ML engineers and product teams to deliver reliable ML-powered features on GPUs.

Qualifications

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience training, fine-tuning, or deploying ML models.
• Ability to write production-quality code and learn new tools quickly.

Responsibilities

• Build and improve ML components across data, training, evaluation, and inference.
• Fine-tune and adapt models for production systems.
• Debug model issues, performance problems, and production incidents.
• Collaborate with senior ML engineers and product teams.

Skills

ML fundamentals

Production systems

Curious mindset

Tools

Python

PyTorch

JAX

GPU deployments

Job description

As a Member of Technical Staff, Machine Learning, you will build core ML components. You will work on real production systems from day one, learning how large-scale ML behaves outside of research settings. This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML.

Overview

• Build and improve ML components across data, training, evaluation, and inference.

• Fine-tune and adapt models as part of larger production systems.

• Implement evaluation and testing to understand model behavior.

• Help build and maintain data pipelines for real-world and synthetic data.

• Debug model issues, performance problems, and production incidents.

• Ship improvements iteratively and learn from real user feedback.

• Work closely with senior ML engineers and product teams.

• Work under real production constraints: latency, cost, reliability, and safety

Tech Stack

• Python

• PyTorch / JAX

• Production ML systems running on GPUs

Ideal Experience

• Strong foundations in machine learning and modern neural architectures.

• Some hands-on experience training, fine-tuning, or deploying ML models.

• Comfortable writing production-quality code and learning new tools quickly.

• Curious, coachable, and eager to learn from real systems in production.

• Able to work through ambiguity with guidance and grow ownership over time.

• Bias toward shipping, iteration, and continuous improvement.

Outcomes

• ML models in production meet expected accuracy, latency, and reliability targets.

• Production issues are identified quickly, debugged effectively, and root causes addressed.

• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.

• Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.

• Iterations on models and systems are driven by real-world signals and measurable improvements.