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- Tasks: Join our Audio AI team to develop innovative audio software solutions using machine learning.
- Company: Cisco WebEx is a leader in technology, dedicated to creating an inclusive digital future.
- Benefits: Enjoy 10 days off for community service and a supportive, collaborative work environment.
- Why this job: Make a significant impact in audio technology while working with a passionate, multidisciplinary team.
- Qualifications: Ph.D. or Master's in relevant field with experience in machine learning and audio processing.
- Other info: Be part of a diverse team that values creativity and innovation in tackling audio challenges.
The predicted salary is between 36000 - 60000 £ per year.
Who are we?
The BabbleLabs team, part of Cisco WebEx. We are a dedicated bunch with strong backgrounds in Electrical Engineering, Machine Learning, Speech Processing and Computer Science. Our areas of expertise include speech enhancement, speech recognition, speech synthesis, and computer vision. But what sets our team apart from the crowd, is our vibrant and unwavering pursuit of excellence.
Overview:
We are seeking a dedicated and innovative Machine Learning expert to join our Audio AI team. As a Speech/Audio Machine Learning Engineer, you will play a crucial role in developing pioneering audio software solutions, using machine learning techniques to enhance audio processing and analysis. You will work closely with a multidisciplinary team of engineers, data scientists, and audio experts to build groundbreaking products that push the boundaries of audio technology. This is a unique opportunity to contribute to the development of next-generation audio software and make a significant impact in the industry.
Key Responsibilities:
- Collaborate with cross-functional teams to design and implement machine learning models and algorithms for audio processing, analysis, and enhancement.
- Train, validate, and fine-tune machine learning models for various applications.
- Evaluate and benchmark the performance of machine learning models using appropriate metrics and statistical techniques.
- Collaborate with software engineers to integrate machine learning algorithms into audio software products and ensure seamless functionality and performance.
- Debug and solve issues related to machine learning algorithms and audio software applications.
- Document software development processes, algorithms, and experiments, and communicate findings and recommendations to the team effectively.
Our Minimum Qualifications for this Role:
- Ph.D. in relevant field with 0+ years or Masters in relevant field with 3+ years of experience in developing and deploying machine learning models for audio related applications.
- Must have strong programming skills in Python and Matlab, with experience in audio processing libraries (e.g., librosa, torch audio, or similar).
- Must understand machine learning techniques, including deep learning architectures (e.g., CNNs, RNNs, GANs) and relevant frameworks (e.g., PyTorch).
- Must be proficient in data preprocessing, feature extraction, and data augmentation techniques for audio.
- Expected to have strong problem-solving skills and ability to think creatively to devise innovative solutions to audio-related challenges.
Our Preferred Qualifications for this Role:
- Familiarity with audio signal processing concepts, such as Fourier analysis, spectral modeling, and time-frequency representations is essential.
Why Cisco?
#WeAreCisco. We are all unique, but collectively we bring our talents to work as a team, to develop innovative technology and power a more inclusive, digital future for everyone. How do we do it? Well, for starters – with people like you!
Nearly every internet connection around the world touches Cisco. We\’re the Internet\’s optimists. Our technology makes sure the data traveling at light speed across connections does so securely, yet it\’s not what we make but what we make happen which marks us out. We\’re helping those who work in the health service to connect with patients and each other; schools, colleges, and universities to teach in even the most challenging of times. We\’re helping businesses of all shapes and sizes to connect with their employees and customers in new ways, providing people with access to the digital skills they need and connecting the most remote parts of the world – whether through 5G, or otherwise.
We tackle whatever challenges come our way. We have each other\’s backs, we recognize our accomplishments, and we grow together. We celebrate and support one another – from big and small things in life to big career moments. And giving back is in our DNA (we get 10 days off each year to do just that).
We know that powering an inclusive future starts with us. Because without diversity and a dedication to equality, there is no moving forward. Our 30 Inclusive Communities, that bring people together around commonalities or passions, are leading the way. Together we\’re committed to learning, listening, caring for our communities, whilst supporting the most vulnerable with a collective effort to make this world a better place either with technology, or through our actions.
So, you have colorful hair? Don\’t care. Tattoos? Show off your ink. Like polka dots? That\’s cool. Pop culture geek? Many of us are. Passion for technology and world changing? Be you, with us! #WeAreCisco
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Audio Machine Learning Engineer employer: Cisco Systems, Inc.
Contact Detail:
Cisco Systems, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Audio Machine Learning Engineer
✨Tip Number 1
Familiarize yourself with the latest advancements in audio machine learning. Follow relevant research papers and attend webinars or conferences to stay updated on cutting-edge techniques that can enhance your understanding and skills.
✨Tip Number 2
Engage with the audio processing community online. Join forums, participate in discussions, and contribute to open-source projects related to audio machine learning. This will not only expand your network but also showcase your passion and expertise.
✨Tip Number 3
Prepare to discuss specific projects where you've applied machine learning techniques to audio problems. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this demonstrates your problem-solving skills.
✨Tip Number 4
Highlight your collaborative experiences in multidisciplinary teams. Since the role involves working closely with engineers and data scientists, showcasing your ability to communicate and collaborate effectively will set you apart from other candidates.
We think you need these skills to ace Audio Machine Learning Engineer
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Audio Machine Learning Engineer position. Understand the key responsibilities and qualifications required, and think about how your experience aligns with them.
Highlight Relevant Experience: In your CV and cover letter, emphasize your experience in developing and deploying machine learning models, especially in audio-related applications. Mention specific projects or technologies you have worked with that relate to the job.
Showcase Technical Skills: Clearly outline your programming skills in Python and Matlab, as well as your familiarity with audio processing libraries. Provide examples of how you've used these skills in past projects to enhance audio processing or analysis.
Communicate Your Passion: In your application, express your enthusiasm for audio technology and machine learning. Share any personal projects or research that demonstrate your commitment to innovation in this field.
How to prepare for a job interview at Cisco Systems, Inc.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning models, especially in audio applications. Highlight specific projects where you used Python or Matlab, and be ready to explain the algorithms and techniques you implemented.
✨Demonstrate Collaboration Experience
Since the role involves working with cross-functional teams, share examples of how you've successfully collaborated with engineers, data scientists, or audio experts in the past. Emphasize your ability to communicate complex ideas clearly.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving skills related to audio processing challenges. Think of scenarios where you had to debug machine learning algorithms or improve model performance, and be ready to walk through your thought process.
✨Familiarize Yourself with Audio Signal Processing
Brush up on key concepts like Fourier analysis and spectral modeling. Being able to discuss these topics will demonstrate your understanding of the field and show that you're well-prepared for the technical aspects of the role.
