Deskripsi Pekerjaan
At Nexus Intelligence, we are architecting the next generation of enterprise AI infrastructure. We are looking for a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale foundation models and RAG systems that power our core product suite.
You will operate at the intersection of research and production, collaborating with a world-class team of researchers and platform engineers to push the boundaries of LLM optimization, distributed training, and low-latency inference. This is a high-impact role with significant autonomy and ownership over the technical direction of our AI stack.
Why Nexus?
- Frontier Tech: Work on custom model architectures, mixture-of-experts (MoE), and advanced quantization techniques.
- Scale: Manage training runs on 1000+ GPU clusters (H100/A100) via Kubernetes/Slurm.
- Ownership: End-to-end lifecycle: data curation → pre-training → RLHF → production serving.
- Compensation: Top-tier base, generous equity (ISO options), 401k matching, and unlimited PTO.
Tanggung Jawab
- Design and implement distributed training pipelines for LLMs (7B–70B+ params) using PyTorch FSDP, DeepSpeed, or Megatron-LM.
- Optimize inference latency and throughput using TensorRT-LLM, vLLM, and custom CUDA kernels for production serving.
- Architect and maintain robust MLOps pipelines (CI/CD/CT) for model versioning, automated evaluation, and canary deployments on Kubernetes (EKS/GKE).
- Lead R&D initiatives in Parameter-Efficient Fine-Tuning (PEFT/LoRA), RAG optimization, and agentic workflow frameworks (LangGraph, LlamaIndex).
- Collaborate with Product and Research to define model evaluation benchmarks (academic & proprietary) aligning with business KPIs.
- Mentor junior engineers, establish coding standards, and drive technical architecture reviews for the ML platform.
- Publish findings internally and externally (blog posts, conferences like NeurIPS, ICML, MLSys) to build Nexus’ brand as an AI leader.
Kualifikasi
- Education: MSc or PhD in Computer Science, Machine Learning, or equivalent practical experience (5+ years industry ML).
- Framework Mastery: Expert-level Python, PyTorch/JAX, and Hugging Face ecosystem (Transformers, Accelerate, TRL).
- Distributed Systems: Proven track record scaling training/inference on multi-node GPU clusters (NCCL, InfiniBand/RoCE networking knowledge).
- MLOps & Cloud: Deep hands-on experience with Kubernetes (operators, schedulers), Docker, Terraform, and major cloud providers (AWS/GCP/Azure).
- LLM Expertise: Intimate familiarity with Transformer architecture, attention mechanisms, rotary embeddings, quantization (GPTQ, AWQ, GGUF), and alignment techniques (DPO, PPO).
- Data Engineering: Experience building petabyte-scale data processing pipelines (Spark, Ray, Polars) for pre-training corpora.
- Soft Skills: Exceptional communication skills; ability to distill complex research concepts for cross-functional stakeholders.