Why This Role Stands Out
Advance the frontiers of AI by leading impactful research in national security, space, and health at Johns Hopkins APL, a role perfect for innovative thinkers who thrive in a collaborative and ethically grounded environment. You'll enjoy opportunities for professional growth and the flexibility of a hybrid work model as you design novel algorithms and mentor future researchers. Apply today to shape the future of artificial intelligence!
Quick Overview
Job Description
Johns Hopkins Applied Physics Laboratory (APL) seeks a Senior Artificial Intelligence Researcher to advance state-of-the-art AI within the Data Science & Artificial Intelligence Department. You will lead original research in machine learning, deep learning, and related fields, tackling high-impact challenges in national security, space exploration, and health. In APL's collaborative, mission-driven, and ethically grounded culture, you will design novel algorithms, publish in top venues, mentor researchers, and transition innovations into real-world systems, while benefiting from strong support for innovation, advanced study, and professional growth.
Responsibilities
- Lead original AI/ML research from problem framing through experimentation and transition to mission applications.
- Design, implement, and evaluate novel models and algorithms for national security, space, and health domains.
- Collaborate with cross-disciplinary teams to translate research advances into deployable systems.
- Publish results in top venues and present findings to sponsors and technical stakeholders.
- Mentor junior researchers and data scientists, fostering a culture of curiosity and ethical research.
- Shape research roadmaps and contribute to proposals and strategic initiatives aligned with APL's mission.
Required Skills
- Deep learning (e.g., transformers, CNNs, RNNs)
- Machine learning theory and algorithms
- Reinforcement learning or probabilistic modeling
- Python and ML frameworks (Py
- Torch, Tensor
- Flow, JAX)
- Large-scale data processing (SQL, Spark, distributed computing)
- MLOps and experiment management tools
- Optimization and statistical inference
- Scientific computing and numerical methods
- Technical writing and publication of research
- Proposal writing and research program development
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