What are the responsibilities and job description for the AI Research Scientist / Machine Learning Engineer (PhD) [33320] position at Stealth Startup?
We are seeking a PhD-level AI Research Scientist or Machine Learning Engineer to develop cutting-edge AI and machine learning solutions that power next-generation products. You will work at the intersection of research and engineering, transforming advanced ML techniques into scalable, production-ready systems.
This role is open to:
- PhD graduates with 4 years of industry experience in AI, machine learning, or applied research.
- Machine Learning Engineers with a PhD and 1–2 years of professional ML engineering experience, provided they have demonstrated success deploying production ML systems.
Responsibilities
- Research, design, and develop advanced machine learning and AI models.
- Build and deploy production-ready ML systems and inference pipelines.
- Collaborate with software engineers to integrate AI capabilities into customer-facing products.
- Optimize model accuracy, efficiency, and scalability.
- Develop data pipelines, model evaluation frameworks, and experimentation workflows.
- Stay current with the latest advancements in machine learning, deep learning, LLMs, and generative AI.
- Mentor engineers and contribute to technical strategy and architecture.
- Publish or present research where appropriate and translate research into business impact.
Qualifications
- PhD in Computer Science, Machine Learning, Artificial Intelligence, Robotics, Statistics, Applied Mathematics, or a related quantitative field.
- 4 years of industry experience in AI/ML research or engineering OR
- 1–2 years of experience as a Machine Learning Engineer with proven experience deploying ML models into production.
- Strong programming skills in Python and experience with C , Java, or Go.
- Hands-on experience with PyTorch, TensorFlow, or JAX.
- Experience building scalable ML pipelines and distributed training or inference systems.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization technologies.
- Strong understanding of algorithms, deep learning, and software engineering best practices.
Preferred Qualifications
- Experience with Large Language Models (LLMs), generative AI, multimodal models, or reinforcement learning.
- Experience with distributed systems, MLOps, Kubernetes, Docker, and CI/CD.
- Publications at leading AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL) are a plus.
- Startup or high-growth technology company experience.
- Strong communication skills with the ability to collaborate across research and engineering teams.