Applied Machine Learning Engineer

Applied Machine Learning Engineer

Location: Singapore (In-Person)

Hiring Entity: Singapore University of Technology and Design (SUTD)

About the Role

We are seeking an Applied Machine Learning Engineer to join a project hosted at the Singapore University of Technology and Design (SUTD), operating at the intersection of research development and hard-core systems engineering.

This role focuses on building and hardening applied ML systems end-to-end, moving from rapid prototypes to reliable, automated pipelines that support multiple concurrent workstreams.

The successful candidate will be employed directly by the Singapore University of Technology and Design (SUTD) and embedded in a small, technically rigorous team operating across research, commercialization, and deployment contexts.

This is not a pure research role. It is a build-and-ship engineering role.

Responsibilities

. Build, extend, and maintain applied ML prototypes, evolving them into robust internal tools and production-ready components

. Develop and iterate ML workflows using modern deep learning tooling, with practical GPU acceleration in Linux environments

. Improve stability, reproducibility, debuggability, and system robustness

. Design and automate end-to-end pipelines, including:

. Data ingestion

. Preprocessing

. Training and inference

. Evaluation

. Reporting

. Run orchestration

. Profile and refine system performance across:

. Throughput and latency

. Memory usage

. Failure recovery

. Monitoring and logging

. Integrate ML components into downstream systems such as batch jobs, services, and internal tools

. Maintain clear documentation, sensible abstractions, and lightweight testing practices

Requirements

. Strong Python engineering skills with clean, maintainable, testable code

. Minimum 2 years of applied ML experience in industry, startup, or research environments

. Experience with deep learning frameworks such as PyTorch or TensorFlow

. Practical GPU computing experience and performance debugging

. Strong Linux proficiency, including environment management, dependencies, containers, and reproducibility

. Ability to work independently, prototype quickly, and manage multiple concurrent workstreams

Desirable Experience

. Startup or research lab experience comfortable with ambiguity and tight iteration cycles

. Computer vision experience

. Experience with agentic workflows or tool-using agents

. Exposure to real-time or near-real-time systems

. Experience designing data pipelines or implementing MLOps patterns

. Familiarity with NumPy, SciPy, and scientific computing practices

. Comfortable reading technical documentation and implementing unfamiliar methods quickly

Research and Commercialization Context

This project collaborates closely with a Singapore-based deep-tech startup spun out of SUTD that is scaling a first-of-its-kind applied AI system for industrial environments. The team designs, builds, and deploys these systems in active field settings, continuously refining novel technology for reliable long-duration deployment.

The candidate must be comfortable operating in an environment where research, engineering, and commercial deployment occur simultaneously. This is not a purely academic research role. The work directly supports a new technology platform that is currently being built and deployed, while the candidate remains formally employed under SUTD.

Because the project sits within an active commercialization pathway, strong contributors may have opportunities to continue working on the technology through either venture development activities or continued research and technical leadership as the programme evolves.