Machine Learning Researcher / Engineer (Foundational Models)

The Machine Learning Researcher/Engineer at Pathway is tasked with performing distributed model training, improving and adapting model architectures based on experimental results, and designing new tasks and experiments. Additionally, you may oversee team members involved in data preparation, making your role vital to the success of foundational projects.

To be considered for the Machine Learning Researcher/Engineer position at Pathway, candidates should meet at least one of the following criteria: have published papers at top-tier conferences like NeurIPS, contributed significantly to newsworthy LLM training efforts, or worked for a leading Machine Learning research center. Furthermore, a strong background in deep learning, familiarity with model training frameworks, and a solid understanding of GPU architectures are essential.

Pathway fosters an intellectually stimulating environment that encourages professional growth. As a Machine Learning Researcher/Engineer, you’ll have the chance to pioneer new AI challenges, collaborate with a talented team, and contribute to impactful research, making this an excellent opportunity for career advancement.

At Pathway, the work culture is collaborative and innovative, emphasizing respect for team members and the importance of diversity in thought. The environment is built on the principles of intellectual curiosity and a shared commitment to advancing foundational AI models, providing a supportive space for creativity.

The compensation for the Machine Learning Researcher/Engineer role at Pathway is highly competitive with a six-digit annual salary based on the candidate's profile and location, alongside an Employee Stock Option Plan, offering both financial growth and investment in the company’s future.

When answering this question, describe the model's architecture, the data you used, and any notable results, emphasizing your role in the project. Highlight the experimental process and what you learned from the training and implementation.

Discuss your experience with distributed training frameworks and techniques. Mention any specific tools you’ve utilized, such as PyTorch Distributed or TensorFlow's Distribution Strategy, and explain how you optimize resource usage and model performance.

Share your debugging approach, ideally including various techniques such as using tensorboard for visualization, running ablation studies, and validating your results against standard benchmarks. Explain how your debugging process helps improve model performance.

Express your views on the evolving landscape of foundational models and how you see them shaping future applications in AI. Mention any technical advancements you find promising and how these could integrate into real-world solutions.

Detail a specific project where your contributions led to meaningful advancements. Discuss the problem, your approach, and the outcome, showcasing how your efforts had a direct impact on the research results.

Discuss your awareness of ethical practices in AI, such as fairness, accountability, and transparency within machine learning. Provide examples of how you’ve incorporated these principles in your past projects.

Explain how effective communication is crucial in collaborating with team members, sharing findings, and iterating on ideas. Provide examples of how clear communication has positively influenced your work and team dynamics.

Mention tools like Git and describe how you leverage them for version control in collaborative projects. Highlight your understanding of CI/CD pipelines and their role in maintaining high-quality codebases.

Discuss methodologies you've employed for monitoring performance, such as establishing key performance metrics, conducting regular evaluations on validation datasets, and using monitoring tools to track real-time performance during deployment.

Share your methods for keeping abreast of advancements, such as following industry leaders on social media, reading recent publications, and participating in conferences or workshops. Talk about how this knowledge influences your work.