Job Description
Nexus AI Labs is at the forefront of the artificial intelligence revolution. We are building the next generation of autonomous systems and predictive analytics tools that will redefine industry standards. We are seeking a visionary Senior Machine Learning Engineer to join our elite R&D team in San Francisco. In this role, you will not just use existing models; you will architect and train the neural networks of tomorrow.
We offer a competitive compensation package, equity options, and an environment that fosters radical innovation. If you are passionate about deep learning, natural language processing, or computer vision and want to work on projects that have a global impact, we want to hear from you.
Why Join Us?
- Work with state-of-the-art hardware (NVIDIA A100 clusters).
- Collaborate with a team of PhD researchers and industry veterans.
- Flexible remote-first policy with a premium office in SOMA.
- Continuous learning budget and access to the world's best AI conferences.
Responsibilities
- Design, develop, and deploy scalable machine learning models and deep learning architectures using Python and TensorFlow/PyTorch.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate research into production-ready applications.
- Optimize model inference for low-latency, high-volume environments and implement MLOps pipelines for continuous training and deployment.
- Conduct rigorous experimentation, hyperparameter tuning, and validation of algorithms to improve model accuracy and robustness.
- Stay abreast of the latest advancements in AI research and integrate cutting-edge techniques into our product suite.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related quantitative field.
- Minimum of 5 years of professional experience in machine learning, deep learning, or AI research.
- Strong proficiency in Python, C++, and SQL.
- Extensive experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and libraries (Hugging Face, Scikit-learn).
- Proven track record of deploying models to production using cloud services (AWS, GCP, or Azure).
- Experience with distributed training, large-scale data processing, and MLOps tools (Docker, Kubernetes, MLflow).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.