Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of autonomous decision-making systems for enterprise logistics. We are looking for a Senior Machine Learning Engineer who thrives at the intersection of cutting-edge research and robust, scalable production engineering.
You will own the full lifecycle of ML models—from ideation and experimentation to deployment and monitoring—working on high-impact problems like real-time route optimization, demand forecasting, and anomaly detection in massive data streams. This is a high-autonomy role for an engineer who wants to see their models drive millions of dollars in operational efficiency.
We offer a competitive compensation package, significant equity upside, comprehensive health benefits, a generous learning stipend, and a culture that values deep work and technical excellence.
Tanggung Jawab
- Design, develop, and deploy scalable machine learning pipelines for real-time inference and batch processing using Python, PyTorch/TensorFlow, and Kubernetes.
- Lead the architecture of MLOps infrastructure (feature stores, model registries, CI/CD for ML) to ensure reproducibility and reliability.
- Collaborate with Product and Research teams to translate ambiguous business requirements into well-defined ML problems with clear evaluation metrics.
- Optimize model performance for latency, throughput, and cost-efficiency on GPU/TPU clusters.
- Implement rigorous A/B testing frameworks and monitoring systems (data drift, concept drift, model degradation) for production models.
- Mentor junior engineers, establish code review standards, and contribute to the team’s technical strategy.
- Stay current with SOTA literature (LLMs, Graph Neural Networks, Reinforcement Learning) and evaluate applicability to our problem space.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field (or equivalent industry experience).
- 5+ years of professional experience building and deploying ML models in a production environment.
- Expert-level proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow/JAX a plus).
- Strong software engineering fundamentals: OOP, design patterns, testing (pytest), version control (Git), and CI/CD (GitHub Actions/GitLab CI).
- Hands-on experience with cloud platforms (AWS/GCP/Azure) and container orchestration (Docker, Kubernetes, Kubeflow/MLflow).
- Proven track record of solving complex problems involving large-scale structured/unstructured data (Spark, Ray, Dask).
- Excellent communication skills with the ability to articulate technical trade-offs to non-technical stakeholders.