Deskripsi Pekerjaan
Are you passionate about building scalable ML systems that power next-generation AI products? Nexus Intelligence Inc. is seeking a Senior Machine Learning Engineer to lead the design, development, and deployment of cutting-edge models into production. You will work at the intersection of research and engineering, collaborating with cross-functional teams to solve complex problems in natural language processing, computer vision, and recommendation systems.
We offer a culture of innovation, significant autonomy, and the opportunity to shape the technical direction of a rapidly growing Series B startup backed by top-tier VCs. If you thrive in a fast-paced environment and want to see your models impact millions of users, we want to hear from you.
Tanggung Jawab
- Design, build, and maintain end-to-end machine learning pipelines (data ingestion, training, evaluation, deployment, monitoring).
- Architect scalable model serving infrastructure using Kubernetes, Docker, and cloud-native tools (AWS/GCP).
- Collaborate with Research Scientists to productionize novel algorithms and optimize model latency/throughput.
- Implement robust MLOps practices: CI/CD for ML, feature stores, automated retraining, and drift detection.
- Mentor junior engineers and define best practices for code quality, testing, and documentation.
- Partner with Product and Data teams to define ML requirements and success metrics for business impact.
- Stay current with SOTA literature (Transformers, Diffusion, LLMs) and evaluate applicability to our roadmap.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- 5+ years of professional experience building and deploying ML models at scale in a production environment.
- Expert proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX).
- Strong software engineering fundamentals: data structures, algorithms, design patterns, and version control (Git).
- Proven experience with cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML) and container orchestration (Kubernetes, Kubeflow).
- Deep understanding of MLOps tools (MLflow, Airflow, Prefect, Weights & Biases) and feature engineering.
- Excellent communication skills with the ability to translate complex technical concepts to non-technical stakeholders.