AI Researcher / ML Engineer (ASR & Speech Specialist)

About this role

ABOUT LILT

AI is changing how the world communicates — and LILT is leading that transformation.

We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.

At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation https://www.linkedin.com/company/intel-corporation/, Canva https://www.linkedin.com/company/canva/, the United States Department of Defense https://www.linkedin.com/company/deptofdefense/, the United States Air Force https://www.linkedin.com/company/united-states-air-force/, ASICS https://www.linkedin.com/company/asics/, and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.

ROLE SUMMARY

We are seeking a highly skilled and visionary Senior AI Researcher / Machine Learning Engineer specializing in Automatic Speech Recognition (ASR) to anchor our core speech intelligence and benchmarking initiatives. In this role, you will serve as our principal subject matter expert in AI speech data processing, responsible for architecting, training, and scaling high-performance, multilingual ASR models, as well as developing rigorous quality benchmarks for agentic conversational AI.

A critical component of this position involves developing robust domain-adaptation frameworks that allow our models to dynamically incorporate proprietary customer terminology, specialized industry jargon, and multilingual nuances. You will collaborate with the Engineering, Product, and AI Research teams to transform state-of-the-art speech research into production-ready systems powering on-device real-time streaming translation and novel frontier model benchmarks.

Key Challenge: Scaling ASR models capable of dynamic vocabulary insertion for enterprise-grade, ultra-low-latency, real-time environments, and end-to-end agentic AI benchmarking that goes beyond surface metrics.

This position may require access to U.S. government systems, facilities, or controlled information. Candidates must be U.S. citizens or nationals, or otherwise eligible to obtain any required government access authorization. LILT will consider all applicants and will work with selected candidates to determine applicable requirements.

KEY RESPONSIBILITIES

- Model Development & Innovation: Architect, train, fine-tune, and evaluate state-of-the-art speech representations and ASR models (e.g., End-to-End Conformer, Whisper, RNN-T, and hybrid CTC/Attention architectures) across multiple global languages.

- Customization & Domain Adaptation: Design and deploy highly scalable algorithms for dynamic vocabulary insertion, contextual biasing, and language model (LM) personalization to precisely capture customer-specific terminology, acronyms, and product names.

- Evaluation: Implement automated framework evaluations to benchmark model performance, rigorously tracking Word Error Rate (WER), Character Error Rate (CER), embedding-based metrics, latency budgets (RTF), and computing efficiency profiles under varying acoustic environments.

- Agentic Benchmarking: Develop pioneering multilingual benchmarks for end-to-end conversational AI agents, including speech-to-text and text-to-speech components, and targeting the weaknesses of state-of-the-art frontier models.

- Real-Time & Batch Speech Systems: Partner with core engineering teams to build, optimize, and maintain high-throughput pipelines optimized for both ultra-low latency real-time streaming inference and high-efficiency asynchronous (batch) multi-channel speech analysis.

- Speech Pipeline Engineering: Develop and refine standard auxiliary components of the speech processing chain, including Voice Activity Detection (VAD), speaker diarization, punctuation restoration, noise/acoustic normalization, and audio pre-processing filters.

- Cross-Functional Productization: Translate product requirements into technical AI roadmaps, working hand-in-hand with Product Managers to ship speech-to-text, simultaneous translation, and semantic speech analytics features.

REQUIRED TECHNICAL QUALIFICATIONS

- Education: Master’s or Ph.D. degree in Computer Science, Electrical Engineering, Computational Linguistics, Data Science, or a related quantitative field with an emphasis on speech processing or deep learning (or equivalent proven industry track record).

- Speech Domain Expertise: Minimum of 3–5 years of dedicated professional experience developing ASR systems, speech-to-text translation pipelines, or advanced audio processing models.

- Deep Learning Frameworks: Advanced proficiency with PyTorch or equivalent frameworks, along with extensive experience utilizing dedicated speech toolkits such as Whisper, NVIDIA NeMo, Hugging Face Transformers, Kaldi, ESPnet, or SpeechBrain.

- On-device runtimes: Hands-on experience converting and running PyTorch models on at least one mobile inference runtime: ExecuTorch, LiteRT (formerly TensorFlow Lite), or ONNX Runtime Mobile. You have personally taken a non-trivial model through conversion, including resolving unsupported operations and dynamic-shape or decoder-loop issues.

- Software & Infrastructure: Strong software engineering principles in Python, with a clear understanding of data structures, algorithm optimization, and handling complex multilingual text/audio tokenization schemas.

- Data Pipeline Mastery: Proven experience working with large-scale audio datasets, audio augmentation techniques (e.g., SpecAugment,

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