Role Description
The Behavioral Intelligence Team within Product Trust Engineering is responsible for building machine learning models and systems to protect our users from the impact of negative content. The mission of the team is to dissuade the use of Dropbox Products by increasing the cost to malicious actors in using our platforms. We embrace the state-of-the-art machine learning technologies and scale them to moderate the tremendous amount of data generated on the platform.
As a Machine Learning Engineer specializing in you will play a pivotal role in safeguarding our users from the adverse effects of negative content. Leveraging your expertise in machine learning and data science, you will develop robust models and systems to detect, mitigate, and prevent the dissemination of harmful content across our platforms.
If you are passionate about leveraging machine learning to protect users and foster a safer online environment, we invite you to join our dedicated team of . Together, we will make a positive impact by building innovative solutions to combat the challenges of negative content and ensure the well-being of our digital communities. Apply now and be part of our mission to shape the future of online trust and safety.
Responsibilities
- Model Development: Design, develop, and deploy machine learning models to detect and classify negative content such as hate speech, harassment, misinformation, and other harmful materials.
- Data Processing and Analysis: Collaborate with cross-functional teams to gather, preprocess, and analyze large-scale datasets to extract meaningful insights and features for model training.
- Algorithm Optimization: Implement state-of-the-art machine learning algorithms and techniques to enhance the accuracy, efficiency, and scalability of our content moderation systems.
- Continuous Improvement: Monitor model performance, conduct regular evaluations, and iterate on existing algorithms to adapt to evolving threats and user behaviors.
- Integration and Deployment: Work closely with software engineers and infrastructure teams to integrate machine learning models into our production systems, ensuring seamless deployment and operation at scale.
Requirements
- BS or MS in Computer Science or related technical field involving Machine Learning, or equivalent technical experience
- 3+ years of experience building machine learning or AI systems
- Strong analytical and problem-solving skills
- Proven software engineering skills across multiple languages including but not limited to Python, C/C++
- Experience with machine learning software packages (e.g., scikit-learn, TensorFlow, Caffe, Theano, Torch)
Desired Qualifications:
- PhD in Computer Science or related field with research in machine learning
- Experience developing models for malware detection or product abuse prevention
- Experience with one or more of the following: natural language processing, deep learning, bayesian reasoning, recommendation systems, learning for search, speech processing, learning from semistructured data, reinforcement or active learning, ML software systems, machine learning on mobile devices
Total Rewards
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.
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 for an IC2 level role (Subject to change):
zł16,700 - zł19,700 - zł22,600 per month
Current Salary/OTE Ranges for an IC3 level role (Subject to change):
zł21,500 - zł25,200 - zł29,000 per month
Dropbox applies increased tax deductible costs (50%) to remuneration earned by certain qualifying employees (to the extent an employee will be involved in the creation of the software as an “author”) for the transfer of copyrights, in accordance with the relevant provisions of the Personal Income Tax Act
