Design, train, and evaluate ML models across multiple domains and modalities — from language and vision to tabular data, time-series, and beyond
Formulate novel research questions, prototype solutions rapidly, and iterate toward publishable and production-ready results
Collaborate cross-functionally with product, engineering, and data teams to identify high-leverage ML opportunities
Champion rigorous experimental practices: sound baselines, reproducibility, ablation studies, and honest reporting of results
Mentor junior researchers and contribute to a culture of intellectual curiosity and high standards
Stay current with the literature across multiple ML subfields and synthesize insights for the broader team
Present research findings internally and externally, including at academic conferences and through peer-reviewed publications
Required Skills
PhD in Machine Learning, Computer Science, Statistics, or a related quantitative field — or equivalent research experience with a strong publication record
5+ years of hands-on ML research experience spanning at least two distinct domains (e.g., NLP, computer vision, RL, time-series, graph ML, generative modeling, etc.)
Demonstrated ability to go from research idea to working implementation: proficient in Python and at least one major ML framework (PyTorch, JAX, or TensorFlow)
Strong grounding in ML fundamentals: optimization, probabilistic reasoning, statistical learning theory, and evaluation methodology
Track record of peer-reviewed publications or equivalent research contributions in competitive venues
Excellent communication skills — able to explain complex technical concepts to both technical and non-technical stakeholders
Experience scaling experiments to large compute clusters (GPU/TPU) and familiarity with distributed training frameworks
Hands-on work with both supervised and self-supervised learning paradigms
Exposure to production ML systems: model serving, monitoring, and iterative deployment
Contributions to open-source ML projects or released research artifacts (datasets, code, model weights)
Background bridging applied and foundational research — comfortable moving between proof-of-concept and product impact
Skills & Requirements
Technical Skills
PythonPytorchJaxTensorflowSupervised learningSelf-supervised learningOpen-source ml projectsReleased research artifactsDatasetsCodeModel weightsCommunicationMentoringMlNlpComputer visionRlTime-seriesGraph mlGenerative modeling