AI/ML Engineer

Conviva•Foster City, CA

21h

About The Position

Conviva is hiring an AI/ML Engineer to develop practical machine learning solutions for real-world problems. The role involves taking ideas from research and experimentation through implementation, while owning key parts of the machine learning development lifecycle: data preparation, model design, training, evaluation, debugging, and iteration. The engineer will assess problems deeply, compare potential approaches, make informed technical decisions, and investigate model outcomes. The role collaborates with engineering and product teams to explore machine learning approaches, develop effective models, and build tools and infrastructure for scalable experimentation and deployment. Candidates may come from academic research or industry, including recent PhD graduates with substantial hands-on research experience. The position is full-time, with an expected salary range of $180,000–$250,000, plus equity and benefits; level and compensation depend on qualifications, background, and experience.

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.
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 willingness to explore new techniques.
Good English communication skills.

Nice To Haves

PhD in Computer Science, Machine Learning, Mathematics, Statistics, or a related quantitative field; PhD is preferred, and recent graduates with strong research experience are encouraged to apply.
Research publications at leading conferences such as NeurIPS, ICML, ICLR, ACL, or 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.

Responsibilities

Design, develop, train, and evaluate machine learning models for complex real-world problems.
Own the 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 where appropriate.
Design rigorous experiments, define meaningful evaluation metrics, establish baselines, and analyze model performance to guide technical decisions.
Translate research ideas into practical solutions in collaboration with engineering and product teams.
Contribute to ML tooling, data pipelines, and infrastructure that support efficient experimentation and scalable model development.
Investigate technical challenges and new approaches, and improve model quality and reliability.
Collaborate with a small technical team, share knowledge, and contribute to engineering best practices.