- Design, build, evaluate, deploy and iterate on large scale Machine Learning systems
- Understand the Machine Learning stack at Dropbox, and build systems that help Dropbox personalize their users' experience
- Work with Product, Design, Infra and Frontend teams to bring your models, and features to life
- Work with large scale data systems, and infrastructure
- Evaluate the performance of machine learning systems against business objectives, and productionize those models
- Contribute to team's technical strategy for the end-to-end machine learning lifecycle, ensuring alignment with business objectives and driving impactful outcomes
- BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
- 8+ years of engineering experience with 5+ years building Machine Learning or AI systems
- Strong industry experience working with large scale data
- Strong collaboration, analytical and problem-solving skills
- Proven software engineering skills across multiple languages including but not limited to Python, Go, C/C++
- Experience with Machine Learning software tools and libraries (e.g., Scikit-learn, TensorFlow, Keras, PyTorch, HuggingFace, LangChain etc.)
- PhD in Computer Science or related field with research in machine learning
- Experience with one or more of the following: natural language processing, deep learning, bayesian reasoning, recommender systems, learning to rank, speech processing, learning from semistructured data, graph learning, reinforcement or active learning, large language models, ML software systems, retrieval-augmented generation, machine learning on edge devices
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Senior Machine Learning Software Engineer, New Initiatives - Remote, United States - Dropbox
Description
Role Description
At Dropbox, our pursuit of excellence in Machine Learning and Artificial Intelligence fuels our mission to empower users worldwide. We've cultivated a culture rooted in meticulous attention to detail, unwavering commitment to reliability, and a drive to innovate at scale.
As a Senior Machine Leaning Engineer, you will be involved in shaping the future direction of the organization and pushing the boundaries on what the world thinks is possible by leveraging the latest advancements in AI/ML. You will join a team of top-tier Machine Learning Engineers and be an inherent part of the product org to create and build delightful new experiences.
Collaborating closely with cross-functional teams, you'll leverage your ML expertise to tackle audacious challenges. Your contributions will directly impact millions of users, as every line of code you write furthers our mission to revolutionize the way people work and collaborate.
Responsibilities
Requirements
Preferred Qualifications
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.
Total RewardsOur 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.
Dropbox takes a number of factors into account when determining individual starting pay, including job and level they are hired into, location/metropolitan area, skillset, and peer compensation. We target most new hire offers between the minimum up to the middle of the range.
Salary/OTE is just one component of Dropbox's total rewards package. All regular employees are also eligible for the corporate bonus program or a sales incentive (target included in OTE) as well as stock in the form of Restricted Stock Units (RSUs).
Current Salary/OTE Ranges (Subject to change):
• US Zone 1: $225,300 - $265,000 - $304,800.
• US Zone 2: $202,700 - $238,500 - $274,300.
• US Zone 3: $180,200 - $212,000 - $243,800.
Dropbox uses the zip code of an employee's remote work location to determine which metropolitan pay range we use. Current US Zone locations are as follows:
• US Zone 1: San Francisco metro, New York City metro, or Seattle metro
• US Zone 2: Austin (TX) metro, Chicago metro, California (outside SF metro), Colorado, Connecticut (outside NYC metro), Delaware, Massachusetts, New Hampshire, New York (outside NYC metro), Oregon, Pennsylvania (outside NYC or DC metro), Washington (outside Seattle metro), Washington DC metro and West Virginia (DC metro)
• US Zone 3: All other US locations
