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Own the full sales cycle to build pipeline, close and renew deals for Dropbox's product suite and emerging AI products. Prospect net-new customers, execute outbound efforts, apply qualification and value-selling frameworks, forecast and manage pipeline, and collaborate cross-functionally to influence GTM and product strategy.
Top Skills:
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The role involves leading application performance improvements across multiple platforms, focusing on measurable metrics and driving engineering standards and workflow optimization, using both hands-on coding and strategic influence.
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Role DescriptionResponsibilities
As a Senior Machine Learning Engineer, you will play a key role in advancing Dropbox’s mission to create a more enlightened way of working. Leveraging cutting-edge AI/ML technologies, you will design, build, deploy, and refine highly reliable AI agents operating at massive scale. Your work will power Dropbox Dash’s universal agentic search and autonomous organization features, transforming how millions of users collaborate, stay organized, and focus on the work that truly matters
Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.
- BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a 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.
Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.
Requirements- BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a 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.
- PhD with a focus on Deep Learning, NLP, or Reinforcement Learning (RLHF/RLAIF).
- Proven track record of taking AI products from concept to launch, either at a massive scale (millions of users) or by leading multiple 0 → 1 cycles in a fast-paced environment.
- Hands-on experience with autonomous agent frameworks, multi-step planning, tool-use (function calling), and advanced RAG.
- Experience with inference optimization, model distillation, or fine-tuning techniques to improve performance and cost-efficiency.
US Zone 1
$245,200—$331,800 USD
US Zone 2
$220,700—$298,700 USD
US Zone 3
$196,200—$265,400 USD
Dropbox Boston, Massachusetts, USA Office
Boston, MA, United States
What you need to know about the Boston Tech Scene
Boston is a powerhouse for technology innovation thanks to world-class research universities like MIT and Harvard and a robust pipeline of venture capital investment. Host to the first telephone call and one of the first general-purpose computers ever put into use, Boston is now a hub for biotechnology, robotics and artificial intelligence — though it’s also home to several B2B software giants. So it’s no surprise that the city consistently ranks among the greatest startup ecosystems in the world.
Key Facts About Boston Tech
- Number of Tech Workers: 269,000; 9.4% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Thermo Fisher Scientific, Toast, Klaviyo, HubSpot, DraftKings
- Key Industries: Artificial intelligence, biotechnology, robotics, software, aerospace
- Funding Landscape: $15.7 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Summit Partners, Volition Capital, Bain Capital Ventures, MassVentures, Highland Capital Partners
- Research Centers and Universities: MIT, Harvard University, Boston College, Tufts University, Boston University, Northeastern University, Smithsonian Astrophysical Observatory, National Bureau of Economic Research, Broad Institute, Lowell Center for Space Science & Technology, National Emerging Infectious Diseases Laboratories
