Job Description
The Opportunity:
Nexus AI Labs is at the forefront of developing next-generation generative models and large-scale neural networks. We are seeking a visionary Senior Machine Learning Engineer to join our elite engineering team in San Francisco. In this role, you will not only build state-of-the-art models but also architect the scalable infrastructure that brings them to life for millions of users. If you are passionate about pushing the boundaries of what is possible with AI and thrive in a fast-paced, high-impact environment, we want to meet you.
Why Join Us?
• Work with cutting-edge technology including Transformers, Diffusion Models, and LLMs.
• Competitive compensation package including equity and comprehensive benefits.
• Flexible remote-first culture with a vibrant San Francisco office.
Your Impact:
You will own the full lifecycle of machine learning projects—from research and experimentation to deployment and monitoring. Your work will directly shape the intelligence of our flagship products, driving efficiency and innovation across the organization.
Responsibilities
- Model Development & Research: Design, train, and fine-tune large-scale machine learning models, with a focus on Natural Language Processing (NLP) and Computer Vision.
- System Architecture: Architect scalable and robust MLOps pipelines using cloud-native technologies (AWS/Azure/GCP) to ensure high availability and low latency.
- Code Quality & Optimization: Write clean, maintainable, and efficient Python code. Optimize model inference speed and reduce computational costs without sacrificing accuracy.
- Collaboration: Partner closely with data scientists, product managers, and software engineers to translate research into production-ready features.
- Monitoring & Evaluation: Implement rigorous monitoring systems to track model performance in production, identify drift, and trigger retraining loops.
- Research Integration: Stay current with the latest academic research in the field and evaluate new techniques (e.g., LoRA, RAG) to integrate into our product stack.
Qualifications
- Education: MS or PhD in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: Minimum of 5 years of professional experience in software engineering or machine learning engineering.
- Technical Skills: Proficiency in Python (PyTorch, TensorFlow, or JAX). Strong understanding of deep learning architectures (CNNs, RNNs, Transformers).
- Tools: Experience with MLOps tools such as MLflow, Kubeflow, Airflow, and containerization technologies (Docker, Kubernetes).
- Big Data: Experience working with distributed data processing frameworks like Spark or Hadoop.
- Problem Solving: Exceptional ability to debug complex issues and optimize system performance under load.