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- Tasks: Identify and evaluate unique speech data sources while building strong relationships with providers.
- Company: Join Aiphoria, a dynamic team creating award-winning AI products for BigTech.
- Benefits: Enjoy competitive pay, remote work, and rapid career growth opportunities.
- Why this job: Make an impact in the exciting field of speech technology and AI.
- Qualifications: Hands-on experience with speech data processing tools and quality assessment metrics.
- Other info: Collaborate with industry experts and work on innovative projects with leading clients.
The predicted salary is between 36000 - 60000 £ per year.
Speech Data Engineer is a key specialist bridging the data market with our technological needs. You will be responsible for identifying unique data sources, evaluating their quality, and building strong relationships with data providers.
Requirements
- Hands‑on experience with speech data processing and labeling tools, such as VAD, Pyannote, whisper, and other segmentation or diarization frameworks.
- Familiarity with quality assessment metrics, including SNR (Signal-to-Noise Ratio) and other acoustic analysis indicators.
- Collect, process, and curate speech datasets, including audio recordings, transcripts, and metadata for multilingual ASR and TTS applications.
- Work closely with internal ASR/TTS development teams to align dataset specifications with model training needs.
- Label and validate audio data, ensuring transcription accuracy, speaker diversity, and consistent metadata standards.
Responsibilities
- Collect and prepare speech datasets (ASR/TTS) across multiple languages when customer data is unavailable.
- Process raw audio data, including speech segmentation, speaker separation, and basic preprocessing.
- Run speech recognition and pseudo‑labeling, and collaborate with crowdsourcing/labeling platforms to improve data quality.
- Understand and apply differences between ASR data (noisy, real‑world speech) and TTS data (clean, high‑quality recordings).
- Organize, version, and maintain speech datasets, ensuring teams always know what data exists and where it lives.
- Support existing data infrastructure and pipelines (e.g. DVC).
- Work with external data providers, evaluating dataset quality and contributing to make‑vs‑buy decisions.
What We Offer
- Experienced team, Aiphoria is formed by a team of enthusiastic professionals who created award‑winning devices, voice assistants and other AI‑driven products for BigTech corporations.
- Cutting‑edge technologies, we build a technology using our areas of expertise including Computer Vision, Speech Technologies, Natural Language Understanding, Generative AI incl. LLM and Diffusion models.
- Rapid career progression, facilitated by our team of seasoned senior professionals who hail from prestigious, industry‑leading companies.
- Remote work opportunities.
- Company has prominent clients with an opportunity for you to work on different projects and/or to be involved in developing our proprietary own products.
- Competitive compensation surpassing market standards.
- A company with entrepreneurial spirit. We offer a unique mix of a secure workspace thanks to the big clients raised along with a true start‑up culture.
Speech Data Engineer employer: Aiphoria
Contact Detail:
Aiphoria Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Speech Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. Building relationships can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with speech data processing tools. Whether it's a project or a case study, having something tangible can really impress hiring managers.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of ASR and TTS applications. Be ready to discuss how you've handled data quality assessments and worked with diverse datasets in the past.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Speech Data Engineer
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your hands-on experience with speech data processing tools like VAD and Pyannote. We want to see how you’ve used these in real-world scenarios, so don’t hold back!
Tailor Your Application: Customise your application to reflect the job description. Mention your familiarity with quality assessment metrics and how you've applied them in past projects. This shows us you’re a perfect fit for the role!
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points if necessary to make your experience easy to read. We appreciate straightforward communication!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Aiphoria
✨Know Your Tools
Make sure you’re familiar with the speech data processing and labeling tools mentioned in the job description, like VAD, Pyannote, and whisper. Brush up on how these tools work and be ready to discuss your hands-on experience with them during the interview.
✨Understand Quality Metrics
Get a solid grasp of quality assessment metrics such as SNR and other acoustic analysis indicators. Be prepared to explain how you’ve used these metrics in past projects to evaluate data quality and ensure accuracy.
✨Showcase Your Dataset Skills
Be ready to talk about your experience collecting, processing, and curating speech datasets. Highlight any specific projects where you’ve worked with multilingual ASR and TTS applications, and how you ensured transcription accuracy and speaker diversity.
✨Collaborate and Communicate
Since this role involves working closely with internal teams and external data providers, demonstrate your ability to build strong relationships. Share examples of how you’ve collaborated in the past to align dataset specifications with model training needs.
