Senior Machine Learning Engineer, NLU & Agentic AI

As a Senior Machine Learning Engineer, NLU & Agentic AI at WerQ AI, your primary responsibilities will include designing and deploying scalable natural language understanding (NLU) models, developing agentic AI solutions for decision-making workflows, and optimizing machine learning models for both accuracy and speed. Collaboration with product and engineering teams is essential to ensure seamless integration of AI solutions, and staying updated on AI trends is crucial for implementing best practices in your work.

To apply for the Senior Machine Learning Engineer position at WerQ AI, you should have proven proficiency in building and deploying NLU and agentic AI systems. A strong background in Python, as well as experience with libraries like TensorFlow, PyTorch, or HuggingFace is required. Additionally, expertise in developing large language models, data preprocessing, and deploying models in cloud environments such as AWS or GCP is highly beneficial.

WerQ AI fosters a collaborative and innovative work culture that's particularly suited to a Senior Machine Learning Engineer. Team members are encouraged to share diverse perspectives and work autonomously while taking ownership of projects. The environment is fast-paced, supportive for growth, and focuses on creating AI solutions that make a meaningful impact, all while emphasizing the importance of people-first innovation.

As a Senior Machine Learning Engineer at WerQ AI, particularly since the company is at a critical growth stage, there are numerous growth opportunities available. You’ll be able to expand your skill set while taking on significant responsibilities that allow for rapid career progression. There are also cross-functional exposure opportunities within the organization, enabling further professional development.

A Senior Machine Learning Engineer at WerQ AI can expect a variety of benefits aimed at enhancing work-life balance and overall well-being. This includes a fully remote and flexible work schedule, unlimited paid time off, and comprehensive health, dental, and vision insurance fully paid by the company. These perks emphasize the company's commitment to supporting its employees both personally and professionally.

Discuss your experience with popular frameworks such as TensorFlow or PyTorch and provide specific examples of projects where you utilized these tools. Mention any challenges you faced and how you overcame them using these frameworks, which would demonstrate your problem-solving abilities and technical proficiency.

Share your hands-on experiences with NLU projects, emphasizing particular models you have developed and the outcomes. Highlight any specific techniques used, such as tokenization or named entity recognition, and discuss how these contributed to solving a problem or enhancing user experience.

Explain your methodology for optimizing models, including techniques like hyperparameter tuning, feature selection, or using efficient algorithms. It’s important to discuss how you've measured both accuracy and speed in your past projects and any tools you've used to facilitate these optimizations.

Discuss your routine for keeping current with AI advancements, such as following relevant publications, joining professional organizations, attending meetups, or participating in online courses. Show your enthusiasm for continuous learning, which is critical in such a rapidly evolving field.

Illustrate your experience collaborating with cross-functional teams, emphasizing your communication strategies to translate technical jargon for non-technical stakeholders. Mention specific examples where your collaborative efforts led to successful integrations of AI solutions.

Use the STAR method to describe a specific challenge related to machine learning. Detail the situation, the task at hand, the actions you took to address it, and the results of those actions. This approach provides a structured response that highlights your problem-solving skills.

Provide insights into your experience with deploying machine learning models using cloud services such as AWS, Google Cloud, or Azure. Discuss any specific challenges and how you ensured effective scaling and performance in a cloud environment.

Outline the metrics you commonly use for evaluating model performance, such as precision, recall, F1 score, or ROC-AUC. Provide examples of how you’ve employed these metrics in past projects and which ones have been most relevant for specific applications.

Share experiences where user feedback influenced model adjustments or refinements. Discuss the feedback-gathering methods you used, how you analyzed the input, and the impact it had on user satisfaction and model performance.

Discuss your passion for AI and machine learning, focusing on your desire to solve real-world problems. Talk about any specific projects or innovations that inspire you, and how you envision your contributions making a significant difference at WerQ AI.