Machine Learning Software Engineer

As a Machine Learning Software Engineer at Zoo, you will be tasked with designing and implementing foundational machine learning systems that improve user experiences in hardware engineering. This includes developing conversational interfaces utilizing LLMs, iterating and fine-tuning models based on user feedback, and collaborating in the design and optimization of large-scale distributed ML systems. Your role will also involve curating large datasets for ML model training and experimenting with various proofs of concept to drive innovation.

To succeed as a Machine Learning Software Engineer at Zoo, you should ideally possess a B.S. in Computer Science or a related discipline, or equivalent professional experience. A demonstrated expertise in machine learning software engineering, hands-on experience with deploying LLM-based applications, and strong programming skills in Python are essential. Additionally, familiarity with CAD software, modeling algorithms, and distributed systems will be highly advantageous for this role.

For the Machine Learning Software Engineer role at Zoo, proficiency in Python is crucial, along with expertise in a low-level programming language such as Rust or C++. Familiarity with machine learning frameworks, particularly PyTorch, is also important to effectively complete your responsibilities in developing robust ML systems.

Machine Learning Software Engineers at Zoo are welcomed into a collaborative and innovative work culture. We emphasize curiosity, user-focus, and the ability to work autonomously while being part of a supportive remote team. Our environment encourages adaptive learning and exploration of new ideas, making it an exciting place for individuals eager to impact the hardware design field positively.

Absolutely! Zoo is committed to fostering growth and advancement for its Machine Learning Software Engineers. Your contributions directly impact our mission, and as we scale our technology and market presence, numerous opportunities for professional development and skill enhancement are available. Engaging in diverse projects and collaborating with a talented team sets the stage for a rewarding career path.

In your response, focus on specific projects where you implemented PyTorch, detailing the types of models you developed and how they contributed to the project's success. Highlight any challenges you faced and how you overcame them using PyTorch's features.

Provide a comprehensive overview of the system, including its architecture, the technologies used, and your specific role in its development. Discuss how you ensured performance, scalability, and manageability throughout the project.

Discuss your methodology for model improvement, including data preprocessing techniques, feature engineering, hyperparameter tuning, and the use of validation datasets. Mention examples where your interventions led to measurable improvements in model performance.

Talk about your practices for dataset creation and curation, emphasizing the importance of quality and relevance. Include any tools you've used for annotation and how you ensure that the datasets remain up-to-date and useful.

Share a specific instance where you applied generative AI techniques to address a challenge, detailing the methods used and the impact of your solution. Highlight any innovative approaches that set your work apart.

Detail your familiarity with CAD tools, explaining how direct usage or understanding of CAD processes has informed your machine learning practices, especially in projects related to hardware design and engineering applications.

Share your approach to debugging, including techniques for identifying errors in data preprocessing, model training, and evaluation phases. Mention specific tools or strategies you prefer to pinpoint and resolve issues effectively.

Discuss the key factors that influence the design of conversational interfaces, such as user experience, response accuracy, and the handling of various user inputs. Provide examples of how you've implemented these considerations in previous projects.

Indicate a specific project where you took initiative and handled significant responsibilities independently. Highlight your ability to manage time effectively, communicate progress, and deliver results without constant oversight.

Reflect on Zoo's mission to revolutionize the CAD industry and how your skills and interests align with this vision. Emphasize your enthusiasm for contributing to innovative projects that have a tangible impact on hardware design and engineering.