Audio AI Engineer

Multilingual Speech-to-Text Engineer On-Device Model Optimization #1085

A Role with Purpose and Impact

This role builds the speech recognition core of a mobile translation capability supporting a government agencys national security mission. The engineer will take large high-quality speech-to-text models spanning many language families and adapt compress and optimize them so they run performantly on an iPhone including handling the reality that speakers frequently mix in borrowed English terms mid-utterance and the model needs to make a sound call on whether to transcribe those terms in English or in the source languages own transliteration.

This is an applied ML role not a research-only position. The strongest candidate can move fluidly from raw audio data to model adaptation and compression experiments to a rigorous evaluation framework and can clearly explain what theyre building why its better than the status quo and how theyll know it worked.

What This Role Is (and Isnt)

This position owns the speech-to-text model its data its training/adaptation its size and latency on-device and its accuracy across languages. It does not own iOS application development translation (source-language-to-target-language) or the Swift/AVFoundation integration layer; those are handled by a separate mobile engineering function this role will collaborate closely with.

Key Responsibilities

• Data pipelines: Ingest clean segment label and version multilingual audio and transcript data with attention to code-switching and borrowed-word phenomena across the target language set.
• Model adaptation: Fine-tune and compress large ASR models (using LoRA/QLoRA quantization distillation or other parameter-efficient and size-reduction techniques as appropriate) to fit iPhone-class memory latency and battery constraints while preserving transcription quality.
• Dynamic per-language deployment: Design model packaging so language-specific weights can be selected and downloaded on demand based on use-case context (e.g. an operator interviewing a Chinese speaker pulls only the Chinese ASR weights).
• Loanword/transliteration handling: Build and evaluate model behavior for deciding when a borrowed English term should be transcribed as-is versus rendered in the source languages transliteration or native equivalent.
• Evaluation: Build reproducible evaluation pipelines (word/character error rate latency robustness to accent/noise/speaking rate/code-switching) and clearly articulate results against defined success criteria for each language and deployment target.
• Documentation & communication: Produce clear model cards dataset documentation and evaluation write-ups that let technical and non-technical stakeholders understand what the model does how it compares to alternatives and what its risks and limitations are.

Required Qualifications

• Bachelors degree in Computer Science Data Science Machine Learning Computational Linguistics or a closely related field.
• Strong data-engineering background building production pipelines for large messy or unstructured audio/text datasets.
• Hands-on experience fine-tuning or adapting speech/audio models using parameter-efficient methods (LoRA QLoRA adapters) and/or model compression techniques (quantization distillation pruning) for constrained hardware.
• Practical experience with ASR/speech-to-text model development and evaluation across multiple languages including error analysis under real-world conditions (accents noise code-switching).
• Strong Python and SQL skills; experience with PyTorch Hugging Face Transformers/PEFT torchaudio librosa or comparable tooling.
• Experience deploying and monitoring production ML systems with an understanding of secure handling of sensitive audio transcripts and derived data in a regulated environment.
• Ability to clearly explain model behavior tradeoffs and limitations to both technical and non-technical stakeholders.

Preferred (Not Required)

• Prior exposure to mobile/on-device ML deployment constraints (even without owning the mobile codebase directly).
• Experience with agentic or multi-step workflow orchestration involving model outputs retrieval or human review.

The estimated salary range for this position is $80000 - $160000. This salary range is not a guarantee of compensation. The offered salary will be based on factors including relevant experience geographic location internal equity and applicable contractual requirements. *Compensation may fall outside this range when appropriate.