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Information Technology 🏢 Full Time ⭐️ Verified

Senior Predictive AI Engineer

Nexus Horizon
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

We are seeking a visionary Senior Predictive AI Engineer to join our elite engineering team in San Francisco. As we prepare for the technological leap of 2026, you will architect the next generation of autonomous systems, driving innovation in predictive modeling and ethical AI.

In this pivotal role, you will bridge the gap between theoretical machine learning breakthroughs and scalable production infrastructure. You will work closely with data scientists and product leads to define the roadmap for our AI evolution, ensuring our solutions are not only cutting-edge but also robust and fair.

Why join us?

• Work on next-gen technology that defines the future.

• Competitive equity and benefits package.

• Remote-first culture with a collaborative hub in SF.

The Role:

You will lead the technical strategy for our core AI engine, optimizing algorithms for speed and accuracy while maintaining strict ethical standards.

Responsibilities

  • Architect and deploy scalable predictive models for high-traffic applications.
  • Collaborate with data engineering teams to optimize data pipelines and feature stores.
  • Research and implement state-of-the-art algorithms (e.g., Transformers, Graph Neural Networks) for 2026-ready tech stacks.
  • Ensure model interpretability, fairness, and compliance with AI ethics guidelines.
  • Mentor junior engineers and conduct code reviews to maintain high technical standards.
  • Translate complex business requirements into technical machine learning solutions.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
  • 5+ years of experience in machine learning engineering, with a focus on predictive analytics.
  • Proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.
  • Strong understanding of distributed systems, cloud architecture (AWS/GCP), and containerization (Docker/Kubernetes).
  • Experience with MLOps tools and deployment pipelines (MLflow, Kubeflow).
  • Proven track record of improving model accuracy and reducing latency.

Required Skills

Python PyTorch TensorFlow Machine Learning MLOps Distributed Systems AWS GCP Docker Kubernetes NLP

Ready to Take This Challenge?

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