Research Engineer, Manipulation

To apply for the Research Engineer, Manipulation role at Scaled Foundations, candidates typically need a Bachelor's degree in Computer Science, Computer Engineering, or a related technical field. Practical experience in machine learning, robotics, computer vision, and natural language processing is also crucial. Ideally, possessing a Master’s or PhD would enhance your candidacy, especially with direct experience in algorithm development and knowledge of deep learning frameworks like PyTorch or Jax.

As a Research Engineer, Manipulation at Scaled Foundations, your responsibilities include designing methods and tools to advance robotic manipulation and foundation models. You'll define research goals based on practical engineering concerns, work with high-fidelity simulation platforms for synthetic data generation, and contribute to experiments by creating reusable code and organizing results, all while publishing and contributing to open-source efforts.

Programming experience is essential for the Research Engineer, Manipulation role at Scaled Foundations. Strong knowledge of programming languages like Python or C++ will help in adapting machine learning models and developing algorithms at scale. Moreover, a good understanding of deep learning frameworks is beneficial for effective implementation.

A Research Engineer, Manipulation at Scaled Foundations can expect to work on exciting projects focused on building generalized robotics foundation models. This includes creating simulation-to-reality pipelines, developing efficient training avenues for large-scale models, and engaging in practical applications of multimodal machine learning to enhance robotic manipulation capabilities.

The work environment for Research Engineers at Scaled Foundations is dynamic and collaborative. You’ll be surrounded by innovative minds passionate about pushing the boundaries of AI and robotics. The team values creativity, experimentation, and knowledge sharing, fostering a culture of growth and learning, ensuring each member contributes to exciting advancements in the field.

Career advancement opportunities for a Research Engineer, Manipulation at Scaled Foundations are promising. Given the company's focus on innovation, there are ample chances for personal growth, whether through further research, leadership roles, or contributing to pioneering projects that can significantly impact the AI and robotics landscape.

A good fit for the Research Engineer, Manipulation role at Scaled Foundations would be someone who is not only technically skilled in robotics and machine learning but also collaborative, innovative, and passionate about developing real-world applications. Candidates who are adaptable and thrive in a dynamic research environment will excel in making meaningful contributions.

When discussing a challenging robotics project, focus on the problem you faced, your approach to solving it, and the technologies you utilized. Provide clear examples of how you adapted your methods to overcome hurdles and what the outcome was, emphasizing any learnings that would benefit your future work.

In designing experiments, outline your systematic approach, including defining your goals, selecting methodologies, and determining evaluation criteria. Be sure to mention the importance of reproducibility and collaboration, and share previous experiences where structured experimentation led to successful outcomes.

When discussing synthetic data generation, mention specific projects where you used simulation platforms to create data sets. Talk about the platforms you utilized and how you ensured the generated data closely mimicked real-world scenarios, enhancing the model training efficiency and accuracy.

Share your experiences with various machine learning frameworks, particularly PyTorch or Jax. Discuss specific situations where you found these frameworks beneficial for implementing deep learning models in robotics, and how they can be leveraged for innovative solutions in robotic manipulation.

Discuss your views on failure as a learning opportunity. Provide an example where you faced a setback in your research, explaining how you analyzed the situation, adapted your strategy, and ultimately succeeded. This demonstrates resilience and a mindset conducive to research.

Collaboration is key in research environments. Mention how you actively seek input from colleagues, engage in brainstorming sessions, and share your findings to enhance group knowledge. Highlight a successful collaborative project as an example of its effectiveness in achieving shared goals.

When explaining sim2real, describe it as the process of transferring knowledge and capabilities learned in simulated environments to real-world applications. Share examples of projects where you've implemented sim2real strategies, emphasizing the challenges and the solutions you developed.

Discuss various methodologies for evaluating robotic models, such as quantifying performance through metrics like accuracy, speed, and efficiency. Describe how you utilize testing environments and results analysis to refine your models and ensure they meet real-world applications effectively.

Share specific innovations in the field of robotics that inspire you. Discuss emerging technologies and applications that excite you, and how you see them being integrated into your future work, emphasizing your enthusiasm for staying up-to-date in a rapidly evolving field.

Explain your approach to adapting standard models for manipulation applications, such as tailoring architectures to specific tasks or incorporating sensory inputs. Providing a concrete example from your experience where you successfully adapted models will showcase your practical understanding and expertise.