Senior/Staff Applied Scientist

For the Senior/Staff Applied Scientist position at Photoroom, candidates should possess over 3 years of experience with machine learning frameworks such as PyTorch, Tensorflow, or Jax. The ideal applicant will have strong expertise in diffusion models for image creation and editing, demonstrating their capability by having trained models from scratch. A pragmatic approach to problem-solving and experience with complex product architectures are also key qualifications.

As a Senior/Staff Applied Scientist at Photoroom, your responsibilities include taking ownership of machine learning work streams, mastering the entire pipeline from training models to implementing them in production. You’ll be working on innovative features and products, quickly iterating based on user feedback, and actively participating in strategic decision-making processes that impact our AI-driven solutions.

The team environment for a Senior/Staff Applied Scientist at Photoroom is multicultural and dynamic, featuring around 75 passionate team members. Collaboration is key, and you’ll be encouraged to share knowledge while working closely with colleagues from diverse backgrounds. The flexible remote work setup allows for a balanced team culture, with regular in-person meetings to foster team cohesion.

Yes! Photoroom offers substantial relocation support for Senior/Staff Applied Scientist candidates, including a €10,000 signing bonus. Our team can assist you in finding accommodation in Paris and provide guidance through the visa application process, making your transition as smooth as possible.

The hiring process for the Senior/Staff Applied Scientist role at Photoroom includes an initial screening call with the Head of AI, where you can discuss your experience and motivation. Following that, candidates will complete a home technical assignment which will be reviewed by the team. This is followed by a culture fit interview where you'll also get to meet your potential future teammates, concluding with a reference check and an offer.

In addressing model training, I prioritize understanding the project requirements and the specific challenges we face. I leverage established frameworks and libraries to ensure efficient development, focusing on iterative testing and validation to optimize model performance. Collaboration with teammates for feedback is crucial during this phase.

Having robust experience with diffusion models, I have actively participated in their development, training them from the ground up. I enjoy exploring novel techniques to enhance image quality and achieving faster results. It’s vital to share insights with the team, fostering a collaborative approach to improve our methodologies.

At my previous position, I led a project where we received user feedback indicating that our model processing times were too slow. I initiated a series of discussions with the team to strategize on optimizing our algorithms and enhancing system efficiency, which subsequently improved user satisfaction.

To ensure quick iteration, I establish a robust feedback loop encompassing user insights, team brainstorming sessions, and performance metrics analysis. I adopt agile methodologies that allow us to adjust our approach swiftly based on real-time data, which helps mitigate any potential bottlenecks.

I foster collaboration by promoting open communication and respect for diverse perspectives. Regular check-ins and team-building activities help create a respectful and inclusive environment where everyone feels comfortable sharing their ideas and expertise.

One challenging project involved optimizing an image recognition model for better accuracy. We encountered numerous issues, including overfitting. By leveraging dropout techniques and data augmentation, we successfully improved the model's performance, illustrating the importance of innovation and adaptability.

Task prioritization in ML is achieved by assessing project deadlines, impact on user experience, and complexity. I often utilize project management tools to keep track of progress and ensure transparency within the team, enabling us to work efficiently together.

I have extensive experience integrating ML models with APIs, ensuring seamless communication between services. I focus on scalability and performance, often conducting stress tests to guarantee our APIs can handle significant loads as we grow.

Staying updated involves following research publications, attending conferences, and engaging with the ML community through forums and social media. I also advocate for knowledge sharing within the team, as it enriches our collective understanding.

Ownership is integral to my work ethic. I believe in taking full responsibility for my projects, which includes being proactive in identifying potential issues and delivering solutions, leading to high-quality outcomes that resonate with our goals.