Job Description
We are looking for a visionary Senior Machine Learning Engineer to join Nebula AI Labs, a pioneering force in generative artificial intelligence. If you are passionate about building scalable, robust, and transformative AI systems that impact millions of lives, this is your opportunity to lead the charge.
In this role, you will bridge the gap between cutting-edge research and production-grade deployment. You will work alongside world-class data scientists and engineers to architect the next generation of intelligent applications. We offer a competitive compensation package, equity options, and the chance to work in a state-of-the-art facility in the heart of Silicon Valley.
Why Join Us?
- Work on projects that define the future of human-computer interaction.
- Competitive salary and equity package.
- Flexible remote-first policy with access to premium office amenities.
- Continuous learning budget and access to the latest hardware.
Responsibilities
- Model Development: Design, implement, and train complex deep learning models using Python, PyTorch, and TensorFlow.
- Infrastructure & Deployment: Build and maintain MLOps pipelines to ensure models are scalable, reproducible, and deployed efficiently to production environments.
- Performance Optimization: Continuously monitor model performance, conduct A/B testing, and optimize inference speed and accuracy.
- Cross-Functional Collaboration: Partner with data scientists, product managers, and engineering teams to translate business requirements into technical solutions.
- Research & Innovation: Stay abreast of the latest advancements in the AI field and integrate novel techniques into our core architecture.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: Minimum of 5 years of experience in machine learning engineering or a similar data science role.
- Technical Skills: Proficiency in Python, SQL, and experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker/Kubernetes).
- Frameworks: Strong hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.