Prescreening Assignment for Engineer Role

Prescreening Interview Assignment for AI Engineer Role ( Job ID : ELV2025-ML)

Role Overview: As an AI Engineer on this project, you will focus on developing and fine-tuning machine learning models for wake word detection, voice cloning, and integrating search functionalities. This prescreening assignment evaluates your expertise in building custom ML models, preparing datasets, fine-tuning LLMs, and understanding search/indexing mechanisms. The assignment should take 4-6 hours to complete. Submit your responses as a PDF report including code snippets, explanations, and any diagrams.

Assignment Tasks:

1. Wake Word Detection Model Design (40% weight): Design a custom neural network architecture for wake word detection in a voice assistant. Assume the wake word is "Hey Assistant."
   • Describe the model architecture (e.g., using CNNs, RNNs, or transformers) and explain why you chose it for low-latency, real-time audio processing.
   • Outline how you would train this model, including data augmentation techniques to handle variations in accents, noise, and environments.
   • Provide pseudocode or a high-level Python snippet (using libraries like PyTorch or TensorFlow) for the model's forward pass and loss function. Discuss potential metrics for evaluation (e.g., precision, recall, false positive rate).
2. Fine-Tuning LLM for Voice Cloning (30% weight): Explain how you would fine-tune a pre-trained LLM (e.g., something like Whisper or a TTS model such as Tacotron) for voice cloning in the context of generating audio responses.
   • Detail the steps to prepare a custom dataset: How would you collect, preprocess, and annotate audio samples for cloning a specific voice? Include considerations for ethical data sourcing and diversity (e.g., multiple speakers, languages).
   • Describe the fine-tuning process, including hyperparameters (e.g., learning rate, batch size) and techniques to avoid overfitting.
   • How would you integrate this with a search engine output to convert text results into cloned voice audio?
3. Search and Indexing Knowledge (30% weight): Describe how search and indexing work in a voice-assisted search engine.
   • Explain the role of inverted indexes, vector embeddings (e.g., using FAISS or Pinecone), and relevance ranking (e.g., BM25 or semantic search with BERT-like models).
   • Propose how to index audio/text data for fast retrieval in response to voice queries, including handling multimodal data (audio + text).
   • Provide a simple example: Sketch a system diagram showing query processing from audio input to indexed search and audio output, highlighting potential bottlenecks.

Submission Guidelines:

• Include references to any papers, tools, or frameworks you mention (e.g., Kaldi for ASR).
• Emphasize trade-offs between accuracy, latency, and resource usage.
• We value clear reasoning over perfect code—focus on problem-solving.

Evaluation Criteria:

• Technical depth and relevance to voice AI.
• Creativity in handling real-world challenges like noisy inputs.
• Clarity and structure of your report.

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Prescreening Interview Assignment for Embedded Systems Engineer Role ( Job ID : ELV2025-EM)

Role Overview: As an Embedded Systems Engineer, you will optimize the end-to-end audio pipeline for low latency in a voice assistant search engine, integrating protocols like RTSP and audio codecs. This prescreening assignment assesses your skills in audio processing, optimization, and embedded hardware/software integration. The assignment should take 4-6 hours. Submit your responses as a PDF report with code snippets, diagrams, and explanations.

Assignment Tasks:

1. Audio Pipeline Optimization for Latency (40% weight): Design an end-to-end audio pipeline for a voice assistant that processes input audio, detects wake words, queries a search engine, and outputs audio responses with minimal latency (target: <500ms round-trip).
   • Map out the pipeline stages: Audio capture, preprocessing, transmission, processing, and output.
   • Explain optimization techniques (e.g., buffering strategies, multi-threading, or hardware acceleration like DSPs). Discuss how to measure and reduce latency at each stage.
   • Provide a high-level C/C++ or Python snippet for a low-latency audio buffer implementation, assuming an embedded platform like Raspberry Pi or ARM-based MCU.
2. Integration of RTSP and Audio Codecs (30% weight): Describe how to use RTSP (Real-Time Streaming Protocol) for streaming audio inputs/outputs in this system.
   • Compare RTSP with alternatives like WebRTC or RTP, and justify its use for low-latency voice assistance.
   • Select and explain two audio codecs (e.g., Opus, AAC) suitable for this project: Discuss compression ratios, bitrate impacts on latency, and compatibility with embedded devices.
   • Outline a setup for encoding/decoding audio streams: Provide pseudocode for integrating a codec library (e.g., FFmpeg or libopus) with RTSP in an embedded environment.
3. Embedded System Considerations and Dataset Preparation Support (30% weight): Address embedded constraints in the voice assistant project.
   • How would you optimize the pipeline for resource-limited hardware (e.g., memory footprint <100MB, power efficiency)? Include trade-offs for running ML models (e.g., wake word detection) on-device vs. edge/cloud.
   • Briefly describe how you'd assist in preparing a custom dataset for audio testing: Focus on tools for recording, formatting (e.g., WAV to compressed formats), and simulating real-time streams on embedded setups.
   • Draw a block diagram of the full system, showing hardware interfaces (e.g., microphones, speakers) and software layers.

Submission Guidelines:

• Reference embedded tools or libraries (e.g., GStreamer for pipelines, ALSA for audio I/O).
• Prioritize practical, implementable solutions over theoretical ones.
• Highlight safety and reliability, such as error handling in streams.

Evaluation Criteria:

• Proficiency in real-time systems and optimization.
• Understanding of protocols and codecs in embedded contexts.
• Practicality and clarity in your explanations.