Job Description
Are you ready to define the standard for Artificial Intelligence in 2026?
Apex Dynamics is at the forefront of the generative AI revolution. We are seeking a visionary Senior AI/ML Engineer to join our elite team in San Francisco. In this pivotal role, you won't just be building models; you will architect the future of intelligent systems that power our global enterprise solutions. If you are passionate about pushing the boundaries of what is possible with Large Language Models (LLMs) and scalable machine learning, we want to hear from you.
Why Join Apex Dynamics?
• Impactful Work: Directly influence the roadmap of our next-generation AI platform.
• Competitive Package: Top-tier salary and equity package for a 2026 visionaries.
• Modern Tech Stack: Work with the latest in PyTorch, TensorFlow, and cloud-native infrastructure.
• Remote-First Culture: Flexible work arrangements with a focus on results.
Responsibilities
- Model Development & Optimization: Design, train, and fine-tune state-of-the-art deep learning models, specifically focusing on LLMs and generative AI architectures.
- Infrastructure Engineering: Build and maintain robust MLOps pipelines using Docker, Kubernetes, and cloud services (AWS/GCP) to ensure high availability and scalability.
- RAG Implementation: Develop and optimize Retrieval-Augmented Generation systems to enhance the accuracy and relevance of AI outputs.
- Performance Tuning: Conduct rigorous testing and optimization to reduce latency and improve inference speed for production environments.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and software engineers to translate complex business requirements into technical solutions.
- Ethical AI Compliance: Ensure all AI models adhere to ethical guidelines, bias mitigation standards, and regulatory requirements.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (PhD preferred).
- Experience: 5+ years of professional experience in machine learning engineering and deep learning.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow; strong understanding of NLP concepts.
- Tools: Experience with MLOps tools (MLflow, Airflow) and version control (Git).
- Problem Solving: Demonstrated ability to troubleshoot complex performance bottlenecks in large-scale systems.
- Communication: Excellent verbal and written communication skills with the ability to explain complex technical concepts to non-technical stakeholders.