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Information Technology 🏢 Full Time ⭐️ Terverifikasi

Senior Machine Learning Engineer

Nexus Intelligence Labs
San Francisco
Estimasi Gaji
USD 180.000 – USD 250.000
Waktu Posting
8 November 2026
Batas Akhir
8 Nov 2027

Deskripsi Pekerjaan

At Nexus Intelligence Labs, we are pioneering the next generation of autonomous AI agents. We are seeking a Senior Machine Learning Engineer to lead the architecture and deployment of large-scale foundation models. This is a high-impact role where you will own the full ML lifecycle—from data curation and distributed training to low-latency serving infrastructure.

You will collaborate with a world-class team of researchers and engineers to push the boundaries of what is possible in Generative AI and Reinforcement Learning. We offer a culture of radical ownership, comprehensive health benefits, unlimited PTO, and a competitive equity package.

Tanggung Jawab

  • Design, train, and optimize large-scale deep learning models (LLMs, Diffusion, Multi-modal) on distributed GPU clusters.
  • Build and maintain robust MLOps pipelines for continuous training, evaluation, and deployment (CI/CD/CT).
  • Implement model optimization techniques (quantization, distillation, pruning) for low-latency inference at scale.
  • Architect feature stores and data pipelines handling petabyte-scale datasets.
  • Collaborate with Product and Research to translate cutting-edge papers into production-grade systems.
  • Mentor junior engineers and establish best practices for code quality, testing, and documentation.
  • Conduct rigorous A/B testing and offline evaluation to measure model quality and business impact.

Kualifikasi

  • M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
  • 5+ years of industry experience building and deploying ML models in production environments.
  • Expert proficiency in Python and deep learning frameworks (PyTorch preferred, TensorFlow/JAX a plus).
  • Deep understanding of transformer architectures, attention mechanisms, and distributed training (FSDP, DeepSpeed, Megatron-LM).
  • Strong engineering fundamentals: Docker, Kubernetes, CI/CD (GitHub Actions/GitLab), Cloud (AWS/GCP).
  • Proven track record of optimizing model inference latency and throughput (TensorRT, vLLM, Triton).
  • Excellent communication skills with the ability to articulate complex technical concepts to cross-functional stakeholders.

Keahlian yang Dibutuhkan

Python PyTorch TensorFlow MLOps Kubernetes AWS Distributed Training LLMs Generative AI Model Optimization CI/CD Feature Stores

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