Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of autonomous decision-making systems. We are seeking a Senior Machine Learning Engineer to lead the design, development, and deployment of scalable deep learning models that power our core product suite. This is a high-impact role where you will own the full ML lifecycle—from raw data exploration and model architecture to production monitoring and optimization.
You will join a world-class team of researchers and engineers backed by top-tier VCs, working in a culture that values intellectual honesty, rapid iteration, and engineering excellence. If you are passionate about pushing the boundaries of LLMs, reinforcement learning, and MLOps infrastructure, we want to talk to you.
Tanggung Jawab
- Design, train, and optimize large-scale deep learning models (Transformers, Diffusion, GNNs) for high-throughput, low-latency inference.
- Build and maintain robust MLOps pipelines (CI/CD/CT) for automated training, validation, versioning, and deployment using Kubernetes, Kubeflow, or Vertex AI.
- Collaborate with Product and Research teams to translate ambiguous business problems into well-defined ML tasks with measurable success metrics.
- Implement advanced techniques for model compression, quantization, and distillation to reduce inference costs on GPU/TPU clusters.
- Establish best practices for data quality, feature stores, experiment tracking (MLflow/W&B), and model observability (drift detection, performance monitoring).
- Mentor junior engineers and conduct rigorous code/architecture reviews to ensure system reliability and maintainability.
- Stay at the forefront of Generative AI research, evaluating and integrating novel architectures (e.g., Mixture of Experts, RAG optimization) into production workflows.
Kualifikasi
- M.Sc. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience (5+ years industry ML experience).
- Expert-level proficiency in Python and modern ML frameworks (PyTorch, JAX, TensorFlow) with a strong grasp of CUDA/Triton optimization.
- Proven track record of deploying and maintaining Large Language Models (LLMs) or Generative AI systems in a production environment.
- Deep expertise in distributed training (FSDP, DeepSpeed, Megatron-LM) and high-performance serving (vLLM, TensorRT-LLM, TGI).
- Strong software engineering fundamentals: Clean architecture, design patterns, testing (pytest), and Git/GitHub workflows.
- Hands-on experience with Cloud platforms (GCP preferred, AWS/Azure acceptable) and Infrastructure-as-Code (Terraform).
- Excellent communication skills; ability to present complex technical concepts to both technical and non-technical stakeholders.