Deskripsi Pekerjaan
At Nexus AI Labs, we are pioneering the next generation of intelligent systems that transform how enterprises leverage data. We are seeking a Senior Machine Learning Engineer to architect, build, and deploy production-grade ML models at scale. You will work alongside a world-class team of researchers and engineers to solve complex problems in natural language processing, computer vision, and recommendation systems.
This is a high-impact role where your work directly shapes our core product and reaches millions of end-users. We value clean code, rigorous experimentation, and a culture of continuous learning. If you are passionate about pushing the boundaries of MLOps and deep learning infrastructure, we want to hear from you.
Tanggung Jawab
- Design and implement end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, and deployment.
- Architect scalable model serving infrastructure using Kubernetes, Docker, and modern MLOps tooling (MLflow, Kubeflow, Airflow).
- Optimize deep learning models (Transformer architectures, CNNs) for latency and throughput on GPU/TPU clusters.
- Collaborate with Research Scientists to translate novel algorithms into robust, maintainable production code.
- Establish best practices for model monitoring, drift detection, automated retraining, and A/B testing frameworks.
- Mentor junior engineers and conduct code reviews ensuring high standards of software engineering and reproducibility.
- Drive the technical roadmap for the ML Platform, evaluating new frameworks and hardware accelerators.
Kualifikasi
- Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- 5+ years of industry experience building and deploying ML models in a production environment.
- Expert proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow/JAX a plus).
- Strong software engineering fundamentals: OOP, design patterns, CI/CD, Git, and Linux/Unix environments.
- Hands-on experience with cloud platforms (AWS/GCP/Azure) and container orchestration (Kubernetes, EKS/GKE).
- Proven track record of optimizing model inference (ONNX, TensorRT, quantization, distillation).
- Excellent communication skills with the ability to articulate complex technical concepts to cross-functional stakeholders.