Job Description
We are Nexus Future Labs, a pioneering force in artificial intelligence and machine learning. We are seeking a visionary Senior AI/ML Engineer to join our elite engineering team in San Francisco. In this role, you will be at the forefront of developing next-generation algorithms that redefine industry standards. If you are passionate about the future of AI, possess deep technical expertise, and thrive in a dynamic, high-growth environment, we want to hear from you.
Why Join Us?
- Work on cutting-edge projects that shape the future of technology.
- Competitive compensation and equity packages.
- Flexible remote-first policy with a vibrant San Francisco office.
- Unlimited PTO and continuous learning budget.
The Role:
As a Senior AI/ML Engineer, you will be responsible for the end-to-end lifecycle of machine learning models, from data engineering and feature selection to deployment and monitoring. You will work closely with cross-functional teams of data scientists, product managers, and engineers to deliver scalable, robust, and ethical AI solutions.
Responsibilities
- Design, develop, and deploy scalable machine learning models and pipelines using Python, TensorFlow, and PyTorch.
- Lead the architecture and optimization of large-scale inference systems to ensure low latency and high throughput.
- Collaborate with data scientists to fine-tune pre-trained models and develop custom architectures for specific use cases.
- Implement rigorous testing and validation strategies to ensure model accuracy, fairness, and robustness.
- Mentor junior engineers and provide technical guidance on best practices in MLOps and data engineering.
- Stay abreast of the latest advancements in AI research and integrate relevant innovations into our product suite.
Qualifications
- Bachelor’s degree in Computer Science, Mathematics, Statistics, or a related field (Master’s or PhD preferred).
- 5+ years of professional experience in machine learning, data science, or a related technical role.
- Strong proficiency in programming languages such as Python, C++, or Java.
- Deep understanding of statistical analysis, probability, and machine learning algorithms.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of shipping production-grade ML models.