What Success Will Look Like
Design, develop, train, and evaluate machine learning models to solve complex real-world problems.
Own the end-to-end ML development lifecycle, including dataset construction, model design, training, experimentation, evaluation, and continuous improvement.
Explore and apply modern ML techniques, including deep learning, reinforcement learning, and LLM training or fine-tuning, as appropriate to the problem.
Design rigorous experiments, define meaningful evaluation metrics, establish baselines, and analyze model performance to guide technical decisions.
Translate research ideas into practical solutions through close collaboration with engineering and product teams.
Contribute to ML tooling, data pipelines, and infrastructure that support efficient experimentation and scalable model development.
Take ownership of technical challenges, proactively investigate new approaches, and continuously improve model quality and reliability.
Collaborate effectively in a small, highly technical team, sharing knowledge and contributing to engineering best practices.
Who You Are & What You've Done
PhD preferred in Computer Science, Machine Learning, Mathematics, Statistics, or a related quantitative field. Recent PhD graduates with strong research experience are encouraged to apply.
Strong foundation in machine learning, deep learning, probability, statistics, algorithms, and data structures.
Hands-on experience developing and training ML models through academic research or industry projects.
Experience with ML frameworks such as PyTorch or TensorFlow.
Solid understanding of the ML development lifecycle, including data preparation, model training, experimental design, evaluation methodology, and error analysis.
Strong programming skills in Python, C++, or another relevant programming language.
Ability to independently investigate complex technical problems, design experiments, and communicate findings clearly.
Strong problem-solving skills, intellectual curiosity, and a willingness to explore new techniques.
Good English communication skills.
Nice to Have
Research publications at leading conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or other relevant venues.
Experience with reinforcement learning, deep learning, LLM fine-tuning, pre-training, or post-training techniques.
Experience with large-scale model training, distributed training, or ML systems optimization.
Experience deploying ML models into production or developing ML infrastructure, including model serving, monitoring, and MLOps.
Familiarity with large-scale data processing frameworks such as Spark or Flink.
The expected salary range for this full-time position is $180,000–$250,000 + equity + benefits. Actual level and compensation are determined by qualifications, professional background, and relevant experience.
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