Conviva is the experience analytics platform for AI agents, apps, and streaming. Conviva pioneered quality of experience measurement for streaming video and has supported live digital experiences across 20 Super Bowls, 10 Olympic Games, and five FIFA World Cup tournaments. With full-census, real-time, stateful analytics, Conviva shows what consumers actually experience, not just whether systems are running, and connects that experience to revenue, retention, and risk.
We are 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.
We are looking for someone who goes beyond implementing existing approaches—someone who can understand a problem deeply, evaluate alternative solutions, make informed technical decisions, and systematically investigate why a model succeeds or fails.
You will collaborate closely with engineering and product teams to explore new ML approaches, develop effective models, and build the tools and infrastructure needed to support scalable experimentation and deployment. We welcome candidates from both academic research and industry backgrounds, including recent PhD graduates with strong hands-on research experience and engineers who have demonstrated exceptional model development capabilities in industry.
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.
