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Information Technology 🏢 Full Time ⭐️ Verified

Senior Machine Learning Engineer

Nexus AI Systems
San Francisco
Salary Estimate
USD 180.000 – USD 240.000
Latest
Live Update
21 Mei 2026
Deadline
21 Mei 2027

Job Description

Join Nexus AI Systems, a pioneering force in the Generative AI revolution. We are seeking a highly skilled Senior Machine Learning Engineer to design and deploy large-scale models that power our next-generation enterprise platforms. If you are passionate about pushing the boundaries of neural architecture and distributed computing, we want to hear from you.

You will work in a high-impact, collaborative environment, utilizing cutting-edge hardware and massive datasets to solve complex real-world problems.

Responsibilities

  • Architect and implement scalable machine learning pipelines using PyTorch and TensorFlow.
  • Optimize model training and inference performance on distributed GPU clusters.
  • Collaborate with data scientists to translate research concepts into production-grade solutions.
  • Monitor and fine-tune model performance metrics, ensuring high accuracy and low latency.
  • Mentor junior engineers and lead code reviews to maintain exceptional engineering standards.
  • Develop robust infrastructure for continuous integration and deployment (CI/CD) of ML models.
  • Stay at the forefront of AI research and integrate innovative techniques into current products.

Qualifications

  • Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related quantitative field.
  • Minimum of 5 years of experience in production-level machine learning engineering.
  • Expertise in Python, C++, and deep learning frameworks (PyTorch, TensorFlow).
  • Deep understanding of cloud computing platforms like AWS, GCP, or Azure.
  • Proven experience with MLOps best practices and containerization (Docker, Kubernetes).
  • Strong proficiency in SQL and familiarity with NoSQL database architectures.
  • Excellent analytical, problem-solving, and communication skills.

Required Skills

Python PyTorch TensorFlow Machine Learning MLOps Kubernetes AWS Distributed Computing

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