Starkey Hearing Technologies Machine Learning Engineer Interview Questions + Guide in 2025

Starkey Hearing Technologies is a pioneering company dedicated to advancing hearing solutions, ensuring that individuals can fully engage with the world around them.

As a Machine Learning Engineer at Starkey, your primary responsibility will be to develop cutting-edge audio processing algorithms and lead machine learning research initiatives. You will collaborate with cross-functional teams to innovate solutions for wearable devices and related accessories, utilizing advanced signal processing and machine learning techniques. Key responsibilities include researching and proposing machine learning and artificial intelligence algorithms, creating proofs of concept, and providing technical leadership within the team. An ideal candidate will possess strong expertise in traditional and modern machine learning techniques, digital signal processing, and programming proficiency in languages such as Python, Matlab, and Java or C++. Additionally, you will be expected to contribute to the broader machine learning community through participation in conferences and intellectual property activities.

This guide will help you prepare effectively for your interview by providing insights into the skills and expertise that Starkey values in a Machine Learning Engineer, equipping you to demonstrate your fit for the role confidently.

The interview process for a Machine Learning Engineer at Starkey Hearing Technologies is structured and thorough, designed to assess both technical expertise and cultural fit within the organization. The process typically consists of several stages, each focusing on different aspects of the candidate's qualifications and potential contributions to the team.

1. Initial HR Call

The first step in the interview process is an introductory call with a Human Resources representative. This conversation usually lasts about 30 minutes and serves as an opportunity for the HR team to provide insights about Starkey and the specific role. Candidates can expect to discuss their background, motivations for applying, and how their skills align with the company's mission and values.

2. Technical Interview 1

Following the HR call, candidates will participate in a technical interview with two senior engineers. This interview focuses on the candidate's resume and past research experiences. Interviewers will delve into the candidate's technical knowledge, particularly in machine learning algorithms and audio processing techniques. Candidates should be prepared to discuss their previous projects in detail and demonstrate their problem-solving abilities.

3. Online Personality Assessment

Candidates will then complete an online personality assessment, which includes a variety of reasoning exercises and basic math problems. While this step may seem less relevant for a senior engineering role, it is designed to gauge the candidate's cognitive abilities and interpersonal skills. Candidates should approach this assessment with an open mind, as it may not directly reflect their technical capabilities.

4. Whole-Day Interview

The next stage is a comprehensive whole-day interview, which may be conducted virtually or in-person. This includes a 60-minute presentation where candidates showcase their previous work, followed by multiple one-on-one interviews with senior engineers and managers. The focus here is on the candidate's research, industry challenges, and collaborative skills. Candidates should be ready to engage in technical discussions and demonstrate their leadership potential.

5. VP Interview

After the whole-day interview, candidates will have a conversation with the Vice President of Advanced Development. This 30-minute discussion typically covers high-level topics such as the role of AI in the industry and the candidate's vision for contributing to Starkey's goals. Candidates should prepare to articulate their long-term aspirations and how they align with the company's strategic direction.

6. Immigration Discussion

For candidates requiring work permits or visas, the final step involves a discussion with the company's immigration specialist. This conversation will explore the logistics of employment and relocation, ensuring that candidates understand the requirements and processes involved.

As candidates prepare for these stages, they should be ready to tackle a variety of interview questions that assess their technical skills, problem-solving abilities, and cultural fit within Starkey.

Here are some tips to help you excel in your interview.

Understand the Company’s Mission

Starkey Hearing Technologies is dedicated to connecting people and changing lives through innovative hearing solutions. Familiarize yourself with their mission and values, and think about how your skills and experiences align with their goals. Be prepared to discuss how your work can contribute to their mission of enhancing the hearing experience for individuals worldwide.

Prepare for a Multi-Stage Interview Process

The interview process at Starkey is comprehensive, often involving multiple stages, including HR calls, technical interviews, and presentations. Be ready to articulate your past research and experiences clearly and confidently. Practice discussing your projects in a way that highlights your problem-solving skills and technical expertise, particularly in machine learning and audio processing.

Showcase Your Technical Skills

Given the emphasis on algorithms and machine learning, ensure you are well-versed in traditional and modern machine learning techniques, including deep learning, SVM, and decision trees. Be prepared to discuss your proficiency in programming languages such as Python and MATLAB, as well as frameworks like TensorFlow and Keras. Consider preparing a portfolio of your work or relevant projects to demonstrate your capabilities.

Emphasize Collaboration and Leadership

Starkey values collaboration across teams. Be ready to discuss your experiences working in cross-functional teams and how you have led projects or mentored junior engineers. Highlight your ability to communicate complex technical concepts to non-technical stakeholders, as this will be crucial in a collaborative environment.

Prepare for Behavioral Assessments

The interview process may include personality assessments and behavioral questions. Reflect on your past experiences and be ready to discuss how you handle challenges, work under pressure, and contribute to team dynamics. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Engage with the Interviewers

During your interviews, engage with your interviewers by asking insightful questions about their work, the team dynamics, and the future direction of Starkey’s technology. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your values.

Clarify Remote Work Policies Early

Given the feedback from previous candidates regarding remote work expectations, it’s essential to clarify any uncertainties about work arrangements early in the process. If remote work is a priority for you, ensure that you discuss this with HR or the interviewers to avoid any miscommunication later on.

Follow Up Thoughtfully

After your interviews, send a thoughtful follow-up email thanking your interviewers for their time. Use this opportunity to reiterate your enthusiasm for the role and briefly mention how your skills align with Starkey’s mission. This can leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you can present yourself as a strong candidate who not only possesses the technical skills required for the Machine Learning Engineer role but also aligns with Starkey’s mission and values. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Starkey Hearing Technologies. The interview process will likely focus on your technical expertise in machine learning algorithms, your experience with audio processing, and your ability to collaborate effectively with cross-functional teams. Be prepared to discuss your past research, technical projects, and how you can contribute to innovative solutions in the hearing technology space.

Machine Learning Algorithms

1. Can you explain the differences between traditional machine learning techniques like linear regression and modern techniques like deep learning?

Understanding the distinctions between these techniques is crucial for a role focused on developing advanced algorithms.

How to Answer

Discuss the fundamental principles of each technique, including their applications, strengths, and weaknesses. Highlight scenarios where one might be preferred over the other.

Example

“Traditional techniques like linear regression are great for simpler problems with linear relationships, while deep learning excels in handling complex, high-dimensional data such as images or audio. For instance, in audio processing, deep learning can capture intricate patterns that traditional methods might miss.”

2. Describe a machine learning project you led. What were the challenges, and how did you overcome them?

This question assesses your leadership and problem-solving skills in a technical context.

How to Answer

Outline the project scope, your role, the challenges faced, and the strategies you employed to address them.

Example

“I led a project to develop a noise-cancellation algorithm for hearing aids. One challenge was the variability in user environments. I implemented adaptive filtering techniques and conducted extensive field tests to refine the algorithm, resulting in a significant improvement in user satisfaction.”

3. How do you approach feature selection for a machine learning model?

Feature selection is critical for model performance, and your approach can reveal your understanding of the process.

How to Answer

Discuss methods you use for feature selection, such as correlation analysis, recursive feature elimination, or domain knowledge.

Example

“I typically start with correlation analysis to identify features that have a strong relationship with the target variable. Then, I use recursive feature elimination to iteratively remove less important features, ensuring the model remains interpretable and efficient.”

4. What are some best practices for training and testing machine learning models?

This question evaluates your knowledge of model development processes.

How to Answer

Mention practices like cross-validation, hyperparameter tuning, and maintaining a separate test set.

Example

“I always use k-fold cross-validation to ensure my model generalizes well. I also perform hyperparameter tuning using grid search to find the optimal settings, and I keep a separate test set to evaluate the final model’s performance.”

5. Can you explain how you would implement a proof of concept (POC) for a new algorithm?

This question assesses your practical approach to algorithm development.

How to Answer

Describe the steps you would take to create and evaluate a POC, including data collection, implementation, and testing.

Example

“To implement a POC, I would first define the problem and gather relevant data. Then, I would develop a prototype of the algorithm using a small dataset to test its feasibility. After initial testing, I would refine the algorithm based on feedback and performance metrics before scaling it up for broader evaluation.”

Digital Signal Processing

1. What is your experience with digital signal processing (DSP) in the context of machine learning?

This question gauges your familiarity with DSP techniques relevant to audio processing.

How to Answer

Discuss specific DSP techniques you have used and how they integrate with machine learning.

Example

“I have worked extensively with Fourier transforms and wavelet transforms to analyze audio signals. These techniques help extract features that are crucial for training machine learning models, particularly in applications like speech recognition and noise reduction.”

2. How would you approach the challenge of real-time audio processing in a wearable device?

This question tests your ability to apply machine learning in time-sensitive applications.

How to Answer

Discuss considerations such as latency, computational efficiency, and algorithm optimization.

Example

“I would focus on optimizing the algorithm for speed, possibly using fixed-point arithmetic to reduce computational load. Additionally, I would implement a pipeline that processes audio in chunks to minimize latency while ensuring high-quality output.”

3. Can you describe a time when you had to troubleshoot a DSP-related issue?

This question assesses your problem-solving skills in a practical context.

How to Answer

Provide a specific example of a DSP issue you encountered and how you resolved it.

Example

“I once faced an issue with audio artifacts in a real-time processing application. After analyzing the signal flow, I discovered that the filter parameters were not correctly tuned. I adjusted the coefficients and re-evaluated the system, which eliminated the artifacts and improved sound quality.”

4. What techniques do you use for audio feature extraction?

This question evaluates your technical knowledge in audio processing.

How to Answer

Discuss various techniques and their applications in machine learning.

Example

“I often use Mel-frequency cepstral coefficients (MFCCs) for feature extraction in audio signals, as they effectively capture the characteristics of human speech. Additionally, I utilize spectrograms to visualize frequency content over time, which can be beneficial for training models.”

5. How do you ensure the robustness of your audio processing algorithms?

This question assesses your understanding of algorithm reliability in diverse conditions.

How to Answer

Discuss methods for testing and validating algorithms under various scenarios.

Example

“I ensure robustness by testing the algorithms across a wide range of audio conditions, including different noise levels and environments. I also implement adaptive algorithms that can adjust parameters based on real-time feedback from the environment.”