全职 Applied Machine Learning Engineer 工作, 薪水, Singapore University Of Technology And Design (SUTD) 公司招聘中 - Ricebowl

About the Role

We are seeking an Applied Machine Learning Engineer to join a project hosted at SUTD, working at the intersection of research and real-world system development.

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 SUTD and embedded in a small, technically rigorous team operating across research and applied deployment contexts.

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

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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

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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

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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

About the Spin-Off Team

This project collaborates closely with Tantanly, a Singapore-based deep-tech startup spun out of SUTD. The team works on applied AI systems that bridge research and commercial deployment in industrial and operational contexts.

While the candidate will be primarily focused on the core SUTD-hosted project, the work overlaps technically with Tantanly’s applied ML systems. As such, the role requires the ability to switch between research-driven development and startup-aligned engineering workstreams when needed, sharing technical foundations across both teams.

• The candidate must be comfortable operating across both environments and contributing to shared technical objectives while remaining formally employed under SUTD.