Senior Machine Learning Engineer, Autonomy

As a Senior Machine Learning Engineer at Woven by Toyota, you'll be responsible for designing and developing advanced machine learning models that are specifically tailored for autonomous vehicles. This includes deploying scalable models on our platforms, integrating new technologies with strict safety standards, and contributing to the end-to-end ML training and deployment process. Collaborating with various teams will be crucial to ensure unified solutions that promote safe and efficient autonomous driving.

To qualify for the Senior Machine Learning Engineer role at Woven by Toyota, you should possess an MS or PhD in Machine Learning, Computer Science, Robotics, or a related field, along with at least three years of industry experience. Proficiency in Python and experience with deep learning frameworks are essential. You should also have strong skills in C++ coding, as well as hands-on experience with various machine learning workflows and techniques.

Woven by Toyota fosters a unique work culture that blends the innovative spirit of Silicon Valley with the rich craftsmanship traditions of Japan. As a Senior Machine Learning Engineer, you will work in a dynamic environment that encourages agility, collaboration, and creativity. We're looking for team players who not only possess technical skills but also have a 'Giver' mindset, focusing on how they can support others in achieving shared goals.

While the minimum qualifications are critical, having additional skills can set you apart as a Senior Machine Learning Engineer at Woven by Toyota. Experience in publishing research at top-tier conferences is a plus, as is familiarity with real-time coding and computer vision technologies. Knowledge of robot motion planning and managing temporal data will also be advantageous in enhancing your contributions to our projects.

Woven by Toyota provides a comprehensive benefits package for its Senior Machine Learning Engineers, which includes excellent health, wellness, dental, and vision coverage, along with a rewarding 401k program. The company promotes a flexible vacation policy to support work-life balance, and also offers family planning and care benefits. Together, these elements ensure that employees can focus on their meaningful work and their personal lives.

In response to this question, highlight specific projects where you've utilized deep learning frameworks such as TensorFlow or PyTorch in the context of autonomous vehicles. Discuss the types of models you developed and how they addressed particular challenges in perception, prediction, or motion planning, emphasizing the impact your contributions made on project success.

When answering this, detail the strategies and techniques you use for model optimization, including methods like pruning, quantization, or hardware acceleration. Mention your experience with deploying models in a production environment and how you ensure their performance meets safety and speed requirements essential in autonomous driving.

Share a specific instance where you encountered a significant obstacle in your ML model development, such as data quality issues or model performance limitations. Describe the steps you took to investigate the issue, any adjustments you made to your approach, and how you eventually achieved success while learning important lessons from the experience.

Discuss your understanding of safety standards in autonomous driving and how they influence your development process. Talk about your methodologies for testing and validation of machine learning models, including simulation, scenario generation, and thorough evaluation against performance metrics to ensure the robustness of your solutions.

Emphasize the importance of teamwork in your previous roles, particularly with cross-functional teams such as software engineers, data scientists, and domain experts. Provide examples of successful collaborations that led to innovative solutions and highlight how you facilitate communication and knowledge sharing among team members.

Detail your experience with data preprocessing techniques you've employed, such as normalization, augmentation, and cleaning. Explain how you approached data curation to ensure high-quality datasets for training your machine learning models, as the quality of input data is crucial in obtaining accurate predictions in autonomous systems.

When answering this, clarify your understanding of ablation studies and why they are important for evaluating the significance of different components in your models. Give an example of an ablation study you've run, the variables you tested, and the insights you gained from the results.

Discuss your engagement with academic literature, online courses, and tech blogs, as well as how you participate in conferences and workshops relevant to AI and machine learning. Mention any communities or networks you're involved with that help facilitate knowledge exchange in the field.

Share examples of your past experiences deploying machine learning models into production environments. Discuss the specific challenges you faced, the technologies or platforms you used, such as cloud services, and how you ensured that the models performed effectively at scale.

Express your openness to feedback and your approach to utilizing it to enhance your models. Share strategies for engaging in constructive discussions with peers and supervisors and how you translate feedback into actionable improvements in your projects.