Job Description
About Nexus Future Systems:
We are pioneering the next generation of artificial intelligence and machine learning solutions for enterprise clients. As a Senior AI Engineer, you will play a pivotal role in designing and deploying scalable, robust, and innovative AI models that define our roadmap for 2024 and beyond. Join a team of world-class engineers and researchers committed to pushing the boundaries of what's possible.
Why Join Us?
- Work with cutting-edge technologies including LLMs, Computer Vision, and Predictive Analytics.
- Competitive salary, equity, and comprehensive benefits package.
- Flexible remote-first policy with a vibrant office culture in San Francisco.
- Continuous learning and professional development opportunities.
Role Overview:
We are seeking a highly skilled Senior AI Engineer to lead our core AI initiatives. You will be responsible for the end-to-end lifecycle of machine learning models, from data ingestion and feature engineering to model training, deployment, and monitoring. You will work closely with product managers and data scientists to translate business requirements into technical AI solutions.
Responsibilities
- Model Development: Design, train, and optimize complex machine learning algorithms and deep neural networks using Python, PyTorch, and TensorFlow.
- System Architecture: Architect scalable AI infrastructure that supports high-throughput data processing and real-time inference.
- Deployment: Implement MLOps pipelines and CI/CD workflows to deploy models to production environments (AWS, GCP, or Azure).
- Collaboration: Partner with cross-functional teams (Data Science, Backend Engineering, Product) to integrate AI features into consumer-facing applications.
- Performance Tuning: Continuously monitor model performance, conduct A/B testing, and optimize for latency, cost, and accuracy.
- Research: Stay abreast of the latest research in AI/ML and evaluate new techniques to apply to our product suite.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in Machine Learning Engineering or Data Science roles.
- Programming: Proficiency in Python and experience with data manipulation libraries (Pandas, NumPy) and SQL.
- Frameworks: Strong hands-on experience with at least one deep learning framework (PyTorch, TensorFlow, JAX).
- Infrastructure: Experience deploying models in cloud environments and familiarity with containerization (Docker) and orchestration (Kubernetes).
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver innovative solutions under tight deadlines.