AI/ML Engineer

Conviva•Foster City, CA

8d•$180,000 - $250,000

About The Position

Conviva is seeking an AI/ML Engineer to develop practical machine learning solutions for challenging real-world problems. The role involves taking ideas from research and experimentation through model development, training, evaluation, debugging, and iteration. The engineer will own key parts of the ML development lifecycle and investigate why models succeed or fail, evaluating alternative approaches and making informed technical decisions. The engineer will collaborate closely with engineering and product teams to explore ML 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 strong hands-on research experience. The expected salary range for this full-time position is $180,000–$250,000, plus equity and benefits. Actual level and compensation depend on qualifications, professional background, and relevant 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.
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 willingness to explore new techniques.
Good English communication skills.

Nice To Haves

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 in Computer Science, Machine Learning, Mathematics, Statistics, or a related quantitative field; recent PhD graduates with strong research experience are encouraged to apply.

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, as appropriate.
Design rigorous experiments, define 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, explore new approaches, and improve model quality and reliability.
Collaborate in a small, highly technical team, sharing knowledge and contributing to engineering best practices.