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Senior Machine Learning Engineer - Generative AI

Aether Logic
San Francisco
Salary Estimate
USD 180.000 – USD 250.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

We are looking for a visionary Senior Machine Learning Engineer to join our elite AI research division. At Aether Logic, we are pushing the boundaries of what is possible with Large Language Models (LLMs) and generative AI. You will work on high-impact projects that redefine user interaction with intelligent systems.


If you are passionate about building scalable, robust, and ethically aligned AI architectures, we want to hear from you.

Responsibilities

  • Design, train, and fine-tune state-of-the-art generative models using PyTorch and TensorFlow.
  • Optimize model inference pipelines to ensure low-latency, high-throughput deployment in production environments.
  • Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate research into production-ready features.
  • Conduct rigorous A/B testing and model evaluation to improve accuracy and reduce bias.
  • Mentor junior engineers and contribute to the team's technical strategy and best practices.
  • Stay abreast of the latest research in NLP, transformers, and deep learning architectures.

Qualifications

  • Master’s or Ph.D. in Computer Science, Mathematics, Statistics, or a related technical field.
  • 5+ years of professional experience in machine learning or AI engineering.
  • Proven expertise in developing and deploying models on cloud platforms (AWS, GCP, or Azure).
  • Strong proficiency in Python, PyTorch, and experience with Hugging Face Transformers.
  • Experience with MLOps tools (Docker, Kubernetes, MLflow) and data orchestration (Airflow).
  • Deep understanding of transformer architectures and fine-tuning methodologies.
  • Excellent problem-solving skills and ability to work in a fast-paced, agile environment.

Required Skills

Python PyTorch TensorFlow Natural Language Processing (NLP) Large Language Models (LLMs) Machine Learning (ML) Deep Learning MLOps Docker Kubernetes AWS GCP SQL

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