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Information Technology 🏢 Full Time ⭐️ Verified

Lead Machine Learning Engineer - 2026 Dynamics

2026 Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
31 Mei 2026
Deadline
31 Mei 2027

Job Description

At 2026 Dynamics, we are pioneering the infrastructure for tomorrow's autonomous world. As a Lead Machine Learning Engineer, you will be at the forefront of developing next-generation algorithms that drive our predictive analytics platform. We are looking for a visionary engineer who combines deep technical expertise with strategic thinking to solve complex problems at scale.


Key Highlights:

  • Work with state-of-the-art AI and neural network architectures.
  • Shape the technical roadmap for a rapidly scaling enterprise.
  • Competitive salary, comprehensive benefits, and equity package.

Responsibilities

  • Design, develop, and deploy scalable machine learning models for large-scale data processing.
  • Lead a team of data scientists and ML engineers to drive innovation and code quality.
  • Collaborate with product managers and stakeholders to define technical requirements and AI strategies.
  • Optimize model performance, latency, and accuracy for production environments.
  • Establish and enforce best practices for MLOps, CI/CD, and data governance.
  • Research and evaluate new technologies and methodologies to improve our competitive edge.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
  • 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
  • Strong proficiency in Python, PyTorch, or TensorFlow.
  • Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
  • Proven track record of leading technical teams and mentoring junior engineers.
  • Excellent communication skills and the ability to translate complex technical concepts to non-technical stakeholders.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning AWS Docker Kubernetes MLOps Data Science

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