Deskripsi Pekerjaan
At Nexus Intelligence Labs, we are architecting the next generation of foundation models for enterprise automation. We are seeking a Senior Machine Learning Engineer to lead the lifecycle of large-scale distributed training and low-latency inference systems. This is a high-impact role where your work directly shapes the core intelligence of products used by Fortune 500 companies.
You will operate at the intersection of research and production, optimizing GPU clusters, designing novel model architectures, and building robust MLOps pipelines. We value deep technical expertise, a bias for action, and the ability to translate complex research papers into scalable, maintainable production code.
Why Join Us?
- Frontier Tech: Work on 70B+ parameter models and cutting-edge RAG architectures.
- Ownership: End-to-end ownership of model performance, from data curation to deployment.
- Compensation: Top-tier base salary, significant equity package (ISO options), and comprehensive benefits.
- Flexibility: Hybrid schedule (3 days onsite in our SoMa hub, 2 days remote).
Tanggung Jawab
- Design, implement, and optimize distributed training pipelines for Large Language Models (LLMs) on multi-node GPU clusters (100+ GPUs).
- Architect high-throughput, low-latency inference services using TensorRT-LLM, vLLM, or custom CUDA kernels.
- Lead the MLOps strategy: Build and maintain CI/CD/CT pipelines for model training, evaluation, versioning, and automated rollout using Kubernetes (EKS/GKE) and Kubeflow/MLflow.
- Conduct rigorous model evaluation and benchmarking (perplexity, latency, throughput, hallucination rates) against SOTA open-source and proprietary baselines.
- Collaborate with Research Scientists to prototype novel architectures (MoE, RWKV, State Space Models) and transition them from notebooks to production-grade code.
- Optimize data processing workflows at petabyte scale using Spark/Ray and vector databases (Pinecone, Weaviate, Milvus) for RAG applications.
- Mentor junior engineers, establish coding standards (Rust/Python), and drive technical architecture reviews.
- Participate in on-call rotations for critical inference APIs, ensuring 99.99% uptime SLAs.
Kualifikasi
- Education: M.S. or Ph.D. in Computer Science, Machine Learning, Mathematics, or equivalent practical experience.
- Experience: 5+ years professional ML engineering experience; 2+ years specifically in Deep Learning / NLP / Generative AI production environments.
- Frameworks: Expert-level proficiency in PyTorch (required), JAX, or TensorFlow. Deep understanding of autograd, distributed data parallel (DDP), FSDP, and activation checkpointing.
- Infrastructure: Hands-on experience with Kubernetes, Docker, Terraform, and cloud providers (AWS preferred: SageMaker, EC2 P4/P5 instances, EFA networking).
- Optimization: Proven track record of model compression (Quantization: GPTQ/AWQ, Pruning, Distillation) and kernel-level optimization (CUDA, Triton, FlashAttention).
- Data Engineering: Strong skills in building scalable data pipelines (Apache Spark, Ray Data, Dagster/Airflow) for unstructured data (text, code, multimodal).
- Software Engineering: Production-grade Python (typing, testing, profiling) and familiarity with Rust or C++ for performance-critical paths.
- Communication: Ability to articulate complex technical trade-offs to both technical and non-technical stakeholders.