Deskripsi Pekerjaan
At Nexus AI Labs, we are building the foundational models that power the next generation of autonomous systems. We are looking for a Senior Machine Learning Engineer who thrives at the intersection of research and production engineering. You will not just implement papers; you will architect scalable training infrastructure, optimize inference latency for real-time edge deployment, and define the MLOps standards for a rapidly scaling team.
This is a high-impact role reporting directly to the CTO. You will own the full lifecycle of model development—from data curation and distributed training on GPU clusters to monitoring drift in production. If you have a track record of shipping LLMs or Computer Vision models at scale and want to solve the hardest problems in AI alignment and efficiency, we want to talk.
Why Nexus?
- Compute Budget: Unrestricted access to H100/A100 clusters via strategic cloud partnerships.
- Open Culture: Publish at top venues (NeurIPS, ICML, CVPR); strong open-source contribution policy.
- Ownership: End-to-end ownership of critical model pipelines, not just ticket-taking.
Tanggung Jawab
- Design, train, and optimize large-scale foundation models (LLMs / Multi-modal) using PyTorch/JAX on distributed GPU clusters (100+ nodes).
- Architect and maintain a robust MLOps platform (Kubeflow, MLflow, Ray) for automated training, evaluation, versioning, and deployment.
- Pioneer model compression techniques (Quantization, Distillation, Pruning, Speculative Decoding) to meet strict latency/cost SLAs for edge and cloud inference.
- Collaborate with Research Scientists to translate novel architectures into production-grade, reproducible codebases.
- Build comprehensive evaluation frameworks (automated benchmarks, human-in-the-loop RLHF/RLAIF pipelines) to measure model quality and safety.
- Own the data flywheel: Design efficient data curation, synthetic generation, and active learning pipelines to continuously improve dataset quality.
- Mentor junior engineers and establish best practices for code review, testing (pytest, property-based), and GPU kernel optimization (Triton/CUDA).
Kualifikasi
- 5+ years industry experience in Machine Learning Engineering; advanced degree (MS/PhD) in CS, Statistics, or related field preferred.
- Expert-level Python and deep mastery of PyTorch (or JAX/TensorFlow); experience writing custom kernels (CUDA/Triton) is a significant plus.
- Proven track record of training and deploying models > 7B parameters in production environments (recommendation systems, LLMs, or Vision Transformers).
- Deep understanding of distributed training paradigms: FSDP, DeepSpeed, Megatron-LM, Pipeline/Tensor Parallelism.
- Strong systems engineering background: Linux internals, networking (RoCE/InfiniBand), containerization (Docker/K8s), and GPU cluster scheduling (Slurm/Kueue).
- Experience with modern LLM tooling: Hugging Face Ecosystem, vLLM/TGI, LangChain/LlamaIndex, Vector DBs (Pinecone/Weaviate/Milvus).
- Demonstrated ability to communicate complex technical concepts to cross-functional stakeholders (Product, Design, Leadership).