Staff Research Engineer - Post Training

As a Staff Research Engineer at P-1 AI, your main responsibilities will include building and deploying advanced AI systems, especially multimodal large language models capable of quantitative reasoning. You will work closely with simulation engineering teams and domain experts to find solutions for real-world challenges, ensuring that you take ownership of various components throughout the entire tech stack. Additionally, you’ll be expected to contribute to the scientific development behind our systems, including algorithm creation and data evaluation.

To be successful as a Staff Research Engineer at P-1 AI, candidates should have a strong engineering background, with proficiency in programming languages like Python and C++. Experience with deep learning frameworks such as PyTorch is essential, along with a solid understanding of machine learning principles. The ideal candidate should also possess skills in debugging ML systems and have familiarity with transformers and related research. A passion for developing AI technologies for the physical realm is a must!

The interview process for the Staff Research Engineer position at P-1 AI consists of several stages designed to assess both technical skills and cultural fit. It begins with a recruiter screen with the Talent Lead, followed by a hiring manager screen with the Head of AI. Candidates will then take part in two technical interviews – the first focuses on software engineering skills through Leetcode challenges, while the second assesses machine learning debugging capabilities and post-training research discussions. Finally, a cultural chat with the CEO provides insight into our company culture.

The Staff Research Engineer position at P-1 AI is primarily a hybrid role, meaning that while there is flexibility for remote work, candidates should ideally be situated in the Bay Area or be open to relocating to San Francisco. This arrangement allows team members to collaborate closely while still enjoying the benefits of a flexible work environment.

In the Staff Research Engineer role at P-1 AI, key technical skills include proficiency in programming languages such as Python and C++, along with expertise in using deep learning libraries like PyTorch. Candidates should be comfortable with writing and debugging machine learning code, and it’s beneficial to have a strong grasp of algorithmic foundations and quantitative reasoning within AI models. A good understanding of recent research in transformers will significantly enhance your contributions to the team.

When answering this question, focus on any specific projects you've worked on involving the integration of different data types—such as text, image, and audio—into a single AI framework. Discuss your role in developing the system, the technologies used, and the impact your contributions had on the project's success.

Prepare a detailed example that outlines the problem, your thought process, and the steps you took to resolve it. Highlight your problem-solving skills and how you collaborated with team members. Emphasize what you learned from the experience and how it has influenced your approach to future projects.

Discuss your approach to testing and validating your code, including practices such as unit testing, integration testing, and performance benchmarking. Highlight the importance of writing clean, maintainable code, and mention any tools or methodologies you employ to monitor and evaluate the effectiveness of your models.

Collaboration is crucial in any research engineering role, especially at P-1 AI. Describe your experiences in multidisciplinary teams, and how you’ve leveraged diverse expertise to overcome challenges. Emphasize your communication skills and willingness to share knowledge and learn from others for successful project outcomes.

Reflect on any deployment challenges you've encountered, such as addressing data quality issues, ensuring model performance in dynamic environments, or dealing with scalability. Discuss the strategies you implemented to tackle these challenges, and what you learned that could help in future deployments.

Mention your commitment to continuous learning through online courses, attending workshops, participating in conferences, and following reputable AI research literature. Highlight any specific resources or communities you engage with to expand your knowledge and stay updated on emerging trends and technologies in the field.

In your response, outline a structured approach to debugging, such as validating input data, checking model architecture, and reviewing training processes. Discuss tools that you utilize, like visualization techniques for understanding model behavior, and emphasize the importance of iteration and experimentation in reaching successful outcomes.

Your answer should reflect an understanding of various evaluation metrics, such as accuracy, precision, recall, or F1 score, based on the specific task at hand. Discuss how the chosen metric aligns with project objectives, and be prepared to explain how you might select and justify the use of different metrics in varying contexts.

Focus on the coding standards you adhere to, such as following style guides, using version control, and documenting your code thoroughly. Discuss the importance of modular design and reusability, as well as tools or techniques you use to ensure code quality and readability for both yourself and others.

Share your enthusiasm for the innovative projects at P-1 AI and your passion for applying AI solutions to tangible, real-world challenges. Reflect on how the company's mission aligns with your career goals and the unique skills you bring that will contribute to the success of the team.