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Information Technology 🏢 Full Time ⭐️ Verified

Senior Machine Learning Engineer

Nexus AI Labs
San Francisco
Salary Estimate
USD 175.000 – USD 230.000
Live Update
10 Mei 2026
Deadline
10 Mei 2027

Job Description

At Nexus AI Labs, we are building the next generation of autonomous intelligence platforms. We are seeking a visionary Senior Machine Learning Engineer to join our core research and development team. You will play a pivotal role in designing scalable architectures that push the boundaries of neural network efficiency and model deployment at enterprise scale.

If you are passionate about deep learning, distributed systems, and solving complex algorithmic challenges, we invite you to help us shape the future of artificial intelligence in the heart of San Francisco.

Responsibilities

  • Architect and implement high-performance machine learning models for production-grade applications.
  • Collaborate with research scientists to translate academic breakthroughs into scalable software solutions.
  • Optimize neural network architectures for inference speed and memory efficiency on cloud infrastructure.
  • Design robust data pipelines for large-scale training and evaluation.
  • Mentor junior engineers and promote best practices in code quality, testing, and documentation.
  • Participate in design reviews and technical strategy planning to drive product innovation.
  • Stay current with emerging trends in NLP, Computer Vision, and Transformer architectures.

Qualifications

  • Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative discipline.
  • 5+ years of experience in deploying production ML models using PyTorch or TensorFlow.
  • Expertise in Python and one or more systems languages like C++ or Rust.
  • Proven track record of managing large-scale distributed training clusters (Kubernetes, AWS/GCP).
  • Strong understanding of mathematical foundations of machine learning and statistical modeling.
  • Excellent communication skills with the ability to bridge the gap between abstract research and practical code.
  • Experience with MLOps workflows and CI/CD for model deployment.

Required Skills

Machine Learning Deep Learning Python PyTorch Kubernetes MLOps Distributed Systems Neural Networks

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