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Senior AI/ML Engineer (Future Tech - 2026 Focus)

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
23 Mei 2026
Deadline
23 Mei 2027

Job Description

Nexus Future Labs is pioneering the next generation of artificial intelligence. We are seeking a visionary Senior AI/ML Engineer to lead the development of cutting-edge Large Language Models (LLMs) and generative AI solutions that will define the technological landscape of 2026 and beyond.

As a key member of our elite R&D team, you will bridge the gap between theoretical research and production-grade deployment. You will work in a dynamic environment where innovation is not just encouraged—it is the standard. If you want to build the AI systems of tomorrow, today, we want to hear from you.

Responsibilities

  • Model Architecture: Design, train, and fine-tune state-of-the-art deep learning models, specifically transformers and diffusion models.
  • Infrastructure: Architect scalable MLOps pipelines to ensure reliable model deployment, monitoring, and retraining strategies.
  • Collaboration: Partner with cross-functional teams of data scientists, software engineers, and product managers to define AI roadmaps.
  • Research: Conduct in-depth research to implement novel algorithms and improve model accuracy and efficiency.
  • Mentorship: Mentor junior engineers and data scientists, conducting code reviews and technical workshops.
  • Ethics & Compliance: Ensure all AI systems adhere to ethical guidelines, safety protocols, and regulatory standards.

Qualifications

  • Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
  • Experience: 5+ years of professional experience in machine learning engineering, NLP, or computer vision.
  • Programming: Expert proficiency in Python, PyTorch, or TensorFlow.
  • Cloud Expertise: Deep understanding of cloud infrastructure (AWS/GCP/Azure) and containerization (Docker/Kubernetes).
  • Production Maturity: Proven track record of deploying high-traffic models to production environments.
  • Soft Skills: Strong communication skills and the ability to translate complex technical concepts for non-technical stakeholders.

Required Skills

Python PyTorch TensorFlow MLOps AWS GCP Kubernetes NLP LLMs Deep Learning Docker Machine Learning Engineering

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