Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of autonomous decision-making systems for enterprise logistics. We are seeking a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale recommendation and optimization models that directly impact millions of daily transactions.
This is a high-autonomy role for a builder who thrives at the intersection of research and production engineering. You will own the full ML lifecycle—from ideation and experimentation using Petabyte-scale datasets to low-latency serving infrastructure. You will collaborate closely with Research Scientists to productionize novel architectures and with Platform Engineers to define the MLOps roadmap.
We offer a culture of intellectual honesty, rigorous engineering standards, and the resources to solve problems that are technically impossible elsewhere. If you want your models to run critical infrastructure at massive scale, this is your environment.
Tanggung Jawab
- Architect and implement end-to-end ML pipelines for real-time inference and batch scoring, ensuring sub-50ms latency SLAs.
- Lead the migration of legacy TensorFlow pipelines to a modern PyTorch/JAX stack with feature store integration (Feast/Tecton).
- Design and execute rigorous A/B testing frameworks and offline evaluation metrics to measure model lift on business KPIs.
- Optimize model serving costs through quantization, distillation, and hardware-aware compilation (TensorRT, XLA).
- Mentor junior engineers on software engineering best practices: CI/CD for ML, testing strategies, and reproducible research.
- Collaborate with Product and Research to translate ambiguous business problems into well-defined ML objectives and loss functions.
- Drive the adoption of LLMs and Retrieval-Augmented Generation (RAG) for internal tooling and customer-facing features.
Kualifikasi
- Master’s or PhD in Computer Science, Statistics, Mathematics, or equivalent practical experience.
- 7+ years of professional experience building and deploying ML systems at scale in a production environment.
- Expert-level proficiency in Python, with deep internals knowledge of PyTorch, JAX, or TensorFlow.
- Proven track record of optimizing GPU/TPU utilization and distributed training (FSDP, DeepSpeed, Megatron-LM).
- Strong software engineering fundamentals: Type-hinting, Docker, Kubernetes, gRPC, and Linux systems programming.
- Experience with modern data stack: Spark/Flink, Iceberg/Delta Lake, Airflow/Dagster.
- Demonstrated ability to communicate complex technical concepts to non-technical stakeholders and influence cross-functional roadmaps.