Machine Learning Engineering Intern developing innovative AI solutions for contact centers at Cresta. Collaborating with top researchers and engineers in AI to unlock customer experience potential.
About the role
- Senior Machine Learning Engineer designing and deploying AI frameworks to enhance Dropbox's collaboration tools. Leading complex ML systems from design to production.
Responsibilities
- Design and productionize agentic AI frameworks — including multi-agent coordination, planning, tool-use, and memory — that allow agents to maintain long-term context and execute complex tasks across the Dropbox ecosystem.
- Lead the end-to-end design of ML systems, from fine-tuning (SFT, RLAIF) and advanced prompting to inference optimization and production monitoring.
- Establish rigorous safety, alignment, and evaluation frameworks to ensure our autonomous systems are helpful, honest, and harmless.
- Collaborate across Product, Design, Infra, and Frontend teams to translate ambiguous user needs into concrete AI capabilities that move the needle for the business.
- Mentor junior engineers and serve as a core contributor to the broader Dropbox AI strategy, fostering a culture of technical excellence.
Requirements
- BS, MS, or PhD in Computer Science, Mathematics, Statistics, or related quantitative field (or equivalent work experience)
- 8+ years of software engineering experience, with at least 5+ years dedicated to building and deploying production-scale AI/ML systems.
- Professional experience in ML modeling for complex systems such as Search, Ranking, or Recommender Systems.
- Deep familiarity with LLM architectures and hands-on experience with ML libraries (e.g., PyTorch, JAX, or similar).
- Strong proficiency in Python (required) and experience with systems languages like Go or C/C++. You should be comfortable building the infrastructure that surrounds the model.
- Extensive experience working with large-scale distributed data systems and high-throughput production environments.
- Exceptional analytical skills and a "bias to action" when navigating ambiguous technical challenges.
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