Machine Learning (ML) Engineer
Summary
Machine Learning Engineer building core ML components for production systems, including data pipelines, model training/fine-tuning, evaluation, and inference under real-world latency, cost, and reliability constraints. Stack centers on Python with PyTorch/JAX running on GPUs.
As a Machine Learning Engineer, 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.
Job Duties:
- 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
- Ensure 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.
Technical Experience/ Requirements:
- Python, PyTorch / JAX, Production ML systems running on GPUs
- 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.
Interested candidates may apply directly to [email protected] (Reg ID: R1103695) for immediate processing
EPS Consultants Pte Ltd |95C5630