At a glance
- Location
- Foster City
About the role
- Conviva is looking for an AI/ML Engineer with strong machine learning fundamentals and hands-on experience in model development, training, and evaluation.
- In this role, you will work on challenging real-world problems across machine learning and AI, taking ideas from research and experimentation to practical solutions.
- You will own key parts of the ML development lifecycle, including data preparation, model design, training, evaluation, debugging, and iteration.
Responsibilities
- 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.
Requirements
- 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.
Preferred qualifications
- 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.
- PhD preferred in Computer Science, Machine Learning, Mathematics, Statistics, or a related quantitative field.
Benefits
- equity
- benefits
Skills
- PyTorch
- TensorFlow
- Python
- C++
- Spark
- Flink
- Deep Learning
- Reinforcement Learning
