Sr. Machine Learning Engineer (Speech) - AI (Bangalore/Noida Locations)
Level AI · Bangalore/Noida, India
About this role
Level AI is a Series C startup revolutionizing customer engagement through an AI-native platform. We are seeking a Sr. Machine Learning Engineer (Speech) to join our AI department and contribute to our advanced speech-to-text pipelines. Roles and Responsibilities: You will work on complex problems in speech-to-text pipelines, including transcription, diarization, and voice activity detection. You will be responsible for training, deploying, and maintaining scalable automatic speech recognition (ASR) pipelines to power our ASR engine. You will stay current with state-of-the-art (SOTA) techniques in ASR, collaborate on system architecture and design, and independently conduct experiments with model architectures and training schemes. Requirements: Candidates must haveat least 2 years of experience and a bachelor's degree in Computer Science or a related field from a tier-1 engineering institute. You must have experience deploying Deep Learning-based models for speech recognition pipelines, strong knowledge of Machine Learning fundamentals, and proficiency in Python and PyTorch. You should also possess an awareness of SOTA research in speech recognition and signal processing, along with experience in algorithm and model development for large-scale applications. The role requires working in an agile environment to deliver high-quality products.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
ASR Pipeline Development and Maintenance
The core responsibility is to build, deploy, and maintain scalable automatic speech recognition (ASR) pipelines. This requires deep proficiency in Python and PyTorch to ensure the engine can handle real-world customer interaction data effectively.
Advanced Speech Processing Research
The role demands staying current with state-of-the-art (SOTA) research in speech recognition and signal processing. You must be able to independently translate academic literature into experimental model architectures and training schemes to improve transcription, diarization, and voice activity detection.
System Architecture Collaboration
You will work closely with team members to design and architect robust systems. This requires balancing technical model performance with the practical constraints of large-scale, high-impact enterprise applications.
Likely challenges to think through
Balancing Research with Production Constraints
The need to stay at the cutting edge of SOTA research while working in an agile environment to deliver high-quality products creates a potential tension between experimental exploration and production stability. Prepare by demonstrating how you prioritize model improvements that provide the highest business value while maintaining system reliability.
Scaling Speech Models for Real-Time Insights
Developing models for large-scale applications often involves trade-offs between latency, accuracy, and computational cost. Be prepared to discuss how you optimize deep learning models for deployment in production environments where real-time performance is critical for customer engagement analytics.
03
Practice your answers
Technical practice
ASR Pipeline Architecture
What to coverDescribe how you would design a scalable ASR pipeline for real-time transcription. Focus on the trade-offs between latency and accuracy, and how you would integrate components like voice activity detection and diarization.
Deep Learning for Speech Recognition
What to coverExplain your experience deploying PyTorch-based models for speech tasks. Discuss how you handle model optimization, quantization, or pruning to ensure efficient inference in a production environment.
Handling Speaker Diarization
What to coverWhat are the primary challenges in implementing speaker diarization for multi-speaker contact center audio? Discuss common approaches such as clustering-based methods versus end-to-end neural diarization.
Voice Activity Detection (VAD) Techniques
What to coverCompare traditional signal processing-based VAD with deep learning-based approaches. When would you choose one over the other in a high-accuracy speech-to-text pipeline?
State-of-the-Art (SOTA) ASR Research
What to coverDiscuss a recent research paper or architecture in the speech domain that you find compelling. Explain how you would evaluate its applicability to a large-scale production system.
Model Training and Experimentation
What to coverDescribe your process for independently conducting experiments with new model architectures or training schemes. How do you track metrics and ensure reproducibility when iterating on ASR models?
Signal Processing Fundamentals
What to coverHow do you handle noisy audio environments, common in contact centers, during the feature extraction phase? Discuss techniques like spectral subtraction or neural-based denoising.
Large-Scale Deployment Challenges
What to coverWhat strategies do you use to maintain and monitor ASR models once they are deployed? Address how you handle data drift or performance degradation over time.
Behavioural practice
Collaborative System Design
What to coverDescribe a situation where you had to collaborate with team members to define a complex system architecture. How did you resolve technical disagreements and ensure the final design met project requirements?
Adapting to Agile Environments
What to coverProvide an example of how you have managed your workload in an agile environment to deliver high-quality ML products. How do you balance the need for rapid iteration with the requirement for robust, production-ready code?
Independent Problem Solving
What to coverTell me about a time you identified a bottleneck in a machine learning pipeline and independently developed a solution. What was your process for testing the new approach and measuring its impact?
Knowledge Sharing and Growth
What to coverHow do you stay current with rapidly evolving AI research, and how have you shared these insights with your team? Give an example of how a new technique you introduced improved a project.
02
Sharpen your knowledge
Topics to prepare
Automatic Speech Recognition (ASR) Architectures
Review modern ASR architectures including Conformer, Transformer-based models, and CTC/Attention-based decoders. Exercise: Compare the performance and latency trade-offs of these architectures for real-time transcription tasks, as this is critical for the company's ASR engine development.
Speech Signal Processing Fundamentals
Deepen understanding of feature extraction techniques like Mel-frequency cepstral coefficients (MFCCs) and filterbanks, as well as noise reduction and voice activity detection (VAD) algorithms. Exercise: Implement a basic VAD module using energy-based or spectral-based methods to understand how to isolate speech from background noise in contact center audio.
Speaker Diarization Techniques
Study the 'who spoke when' problem, focusing on clustering-based and neural diarization approaches. Exercise: Research current SOTA methods for handling overlapping speech and evaluate how these would scale in a high-volume, multi-speaker customer service environment.
Skills to revise
Deep Learning Model Deployment with PyTorch
Focus on optimizing models for production, including quantization, pruning, and using TorchScript or ONNX for inference. Exercise: Take a pre-trained speech model and convert it to a serialized format, then measure the latency improvement on a sample audio stream to simulate production deployment.
Large-Scale Machine Learning Pipeline Development
Develop skills in building scalable data ingestion and training pipelines. Exercise: Design a modular Python pipeline that handles audio data preprocessing, batch training, and automated evaluation metrics, ensuring it can handle large datasets typical of enterprise contact centers.
04
Your preparation checklist
Review ASR pipeline architecture
Prepare a technical overview of how you have previously designed or maintained end-to-end speech-to-text pipelines, specifically addressing challenges in transcription, diarization, and voice activity detection.
Demonstrate PyTorch model deployment
Prepare a code walkthrough or case study showcasing a deep learning model you deployed into production using PyTorch, focusing on scalability and performance optimization for large-scale applications.
Synthesize SOTA speech research
Be ready to discuss recent advancements in speech recognition and signal processing research, and explain how you would evaluate and integrate these techniques into an existing production ASR engine.
Prepare for experimental design questions
Review your experience in conducting independent experiments; prepare to explain your methodology for testing new model architectures, tuning training schemes, and validating results against benchmarks.
Formulate questions on AI-native infrastructure
Prepare thoughtful questions regarding how Level AI integrates Large Language Models with ASR outputs to drive customer engagement insights, demonstrating your interest in the company's specific AI-native platform.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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