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The AI Scientist / Engineer – Speech Language Models leads the design, development, and evaluation of AI systems for language assessment, real‑time feedback, and skills evaluation. The role focuses on speech recognition, spoken language modeling, and automated scoring, ensuring models are accurate, reliable, fair, and scalable across learner‑facing and hiring‑facing applications.

• Design, build, and improve speech language models for spoken response understanding, pronunciation analysis, fluency, prosody, and communicative effectiveness.
• Develop and evaluate automated scoring and feedback pipelines for speaking tasks used in:
  • AI‑driven speaking practice with instant feedback (learner‑facing).
  • Job‑relevant oral communication and soft‑skills assessments (hiring‑facing).
• Train, fine‑tune, and evaluate acoustic models, cascading models, speech‑to‑speech models, speech LMs, and scoring models, including neural and large language model–based approaches.
• Design experiments and conduct quantitative performance, reliability, and validity analyses to ensure assessment quality and decision integrity.
• Work across a range of speaking constructs such as interactional competence, pragmatic competence, spoken critical thinking skills etc.
• Perform detailed error analysis, intra- and inter-agent rater reliability studies on ASR outputs, spoken features, and scoring behaviors to guide model and product improvements.
• Collaborate with product, UX, and assessment scientists to integrate models into interactive experiences such as practice simulations, and hiring workflows.
• Apply responsible AI principles to speech systems, including fairness across accents, dialects, and proficiency levels, as well as transparency of feedback and scores.
• Support model monitoring and governance in production environments, ensuring ongoing quality and compliance for high‑stakes use cases.
• Act as the technical lead for an AI conversational assessment product, partnering closely with a Product Manager to translate assessment goals, user needs, and business constraints into model and system design decisions.
• Shape end‑to‑end conversational assessment design (task structure, prompts, turn‑taking, scoring logic, feedback timing) in collaboration with product and assessment stakeholders.
• Balance assessment validity, user experience, system latency, and scalability when making model and system design trade‑offs for production conversational assessments.

• Master’s or PhD in Computer Science, Electrical Engineering, Speech & Language Processing, Applied Linguistics, Language Assessment, or equivalent applied experience.
• Hands‑on experience building speech recognition, spoken language understanding, or automated scoring systems.
• Strong programming skills in Python, with experience using PyTorch or similar ML frameworks for speech and language modeling.
• Solid grounding in machine learning, statistics, and experimental design, especially as applied to model evaluation.
• Experience with modern neural speech models and large language models, including fine‑tuning and evaluation for spoken tasks.
• Expertise in model evaluation metrics relevant to speech and assessment (accuracy, reliability, validity, fairness).
• Familiarity with responsible AI practices, including bias analysis, interpretability, and governance for user‑impacting systems.
• Strong communication skills, with the ability to explain model behavior and assessment outcomes to technical and non‑technical stakeholders.
• Experience working in cross‑functional product teams, contributing to roadmap decisions, and shipping ML systems into production.

• Experience with spoken feedback systems, pronunciation scoring, fluency analysis, or conversational AI.
• Background in skills assessment, talent evaluation, or hiring platforms using AI‑based decision support.
• Familiarity with human‑in‑the‑loop evaluation, rater alignment, or psychometric concepts for AI scoring systems.

This role directly enables:

• Learner‑facing speaking practice with immediate, actionable feedback powered by speech LMs.
• Hiring‑grade oral communication and skills assessments that support fair, data‑driven talent decisions.
• Scalable, responsible speech AI systems that balance technical excellence with assessment validity.

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