Job Specification - Voice Research Supervisor

🎯 Role Definition

The Voice Research Supervisor holds a pivotal role at the intersection of scientific innovation and team leadership. This individual is responsible for directing a team of highly skilled research scientists focused on developing and improving core voice technologies, including automatic speech recognition (ASR), text-to-speech (TTS), speaker recognition, and natural language understanding (NLU).

This position is not just about technical expertise; it's about inspiring a team, setting a strategic research direction, and acting as the crucial bridge between foundational research and product application. The supervisor ensures that the team's groundbreaking work aligns with the company's strategic objectives, translating complex research into tangible technological advancements that shape the future of human-computer interaction.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Speech Scientist
Lead Machine Learning Engineer (Speech/Voice)
Principal Research Scientist (with a desire to move into management)

Advancement To:

Director of AI Research
Senior Manager, R&D
Head of Voice Technology

Lateral Moves:

Principal Scientist (returning to an individual contributor leadership track)
AI/ML Product Manager
Solutions Architect, Voice & Conversational AI

Core Responsibilities

Primary Functions

Lead, mentor, and manage a dedicated team of voice and speech research scientists, fostering a culture of innovation, intellectual curiosity, and collaborative excellence.
Define, architect, and drive the long-term research roadmap for next-generation speech and language technologies, ensuring alignment with overarching business goals.
Oversee the complete research and development lifecycle, from initial ideation and literature review to model architecture design, experimentation, and the delivery of deployment-ready prototypes.
Establish, track, and report on key performance indicators (KPIs) for model performance, including Word Error Rate (WER), Mean Opinion Score (MOS), latency, and computational footprint.
Collaborate cross-functionally with product management, software engineering, and MLOps teams to translate high-level business requirements into concrete technical research objectives.
Act as the team's primary technical guide, providing hands-on support and expert direction on complex research challenges in areas like far-field audio, noise robustness, and low-resource languages.
Stay at the absolute forefront of academic and industry advancements in machine learning, deep learning, NLP, and speech science, disseminating key findings and insights to the team and wider organization.
Direct the strategic design and execution of large-scale data collection, annotation, and augmentation initiatives to continuously improve acoustic and language models.
Guide the team in selecting and implementing the most appropriate algorithms, frameworks (e.g., PyTorch, TensorFlow), and specialized toolkits (e.g., Kaldi, ESPnet, NeMo).
Manage project priorities, resource allocation, and timelines for the voice research team, ensuring the timely and successful delivery of critical research milestones.
Champion the professional and technical growth of team members through constructive performance reviews, continuous coaching, and identifying opportunities for skill development and conference attendance.
Drive the creation of intellectual property by encouraging and guiding the team through the process of patent filing and publishing research in top-tier academic venues.
Clearly and effectively present complex research findings, project status updates, and strategic recommendations to senior leadership and other non-technical stakeholders.
Architect and advocate for scalable research infrastructure, data pipelines, and experimental platforms in close partnership with infrastructure and data engineering teams.
Ensure the ethical and responsible development of voice technologies, proactively identifying and mitigating potential biases in data and models.
Formulate strategies for efficient model training, optimization, and quantization for deployment across a spectrum of environments, from cloud infrastructure to resource-constrained edge devices.
Uphold and enforce rigorous standards for research code quality, experimental reproducibility, and documentation through peer reviews and best practices.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis to uncover novel insights and inform future research directions.
Contribute expert perspective to the organization's broader data strategy and AI technology roadmap.
Collaborate with business units like customer experience and marketing to translate their voice interaction needs into actionable engineering and research requirements.
Participate in sprint planning, retrospectives, and other agile ceremonies to ensure seamless integration of research outputs into the product development pipeline.
Engage with the external research community by attending and presenting at major conferences (e.g., Interspeech, ICASSP), reviewing papers, and building university partnerships.
Play a key role in the talent acquisition process, including screening resumes, conducting technical interviews, and onboarding new scientists to build a world-class team.

Required Skills & Competencies

Hard Skills (Technical)

Expertise in Core Speech Technologies: Authoritative, deep knowledge of the theory and practice behind Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and Speaker Identification/Verification systems.
Machine Learning & Deep Learning Mastery: Proven proficiency in designing, training, and debugging modern neural network architectures, particularly those relevant to speech like Transformers, Conformer models, CNNs, and RNNs/LSTMs.
Advanced Programming Proficiency: Expert-level skills in Python for scientific computing and rapid prototyping, along with a strong familiarity with C++ for optimizing performance-critical code.
ML Framework Fluency: Extensive hands-on experience with at least one major deep learning framework, such as PyTorch (preferred) or TensorFlow.
Speech Toolkit Experience: Practical experience utilizing and/or modifying open-source speech toolkits like Kaldi, ESPnet, or NVIDIA NeMo.
Digital Signal Processing (DSP): A strong theoretical and practical understanding of audio signal processing, acoustic feature extraction (e.g., MFCCs, filter banks), and acoustic modeling.
Applied Natural Language Processing (NLP): Solid grasp of language modeling, text normalization, tokenization, and other NLP concepts as they apply to building robust speech systems.
Cloud & MLOps Familiarity: Working knowledge of cloud computing platforms (AWS, GCP, or Azure) and the principles of MLOps for managing the lifecycle of machine learning models.

Soft Skills

Inspirational Leadership & Mentorship: A demonstrated ability to lead, grow, and empower a team of highly intelligent and autonomous researchers, acting as a coach rather than just a manager.
Strategic Vision: The ability to look beyond immediate tasks to define a compelling long-term research vision that anticipates future trends and aligns with strategic business imperatives.
Exceptional Communication: The skill to articulate highly complex technical concepts with clarity and confidence to diverse audiences, from junior scientists to C-level executives.
Pragmatic Problem-Solving: A creative, first-principles approach to deconstructing and solving novel, ill-defined research problems in a systematic and efficient manner.
Agile Project Management: Strong organizational capabilities to effectively manage multiple, often ambiguous, research projects, balancing long-term goals with short-term deliverables.

Education & Experience

Educational Background

Minimum Education:
A Master's Degree in a relevant technical field.

Preferred Education:
A Ph.D. is strongly preferred, as it demonstrates a deep background in independent research and contribution to the field.

Relevant Fields of Study:

Computer Science
Electrical Engineering
Computational Linguistics
Artificial Intelligence
Physics, Mathematics, or a related quantitative discipline with a focus on machine learning.

Experience Requirements

Typical Experience Range:
8-12 years of professional, post-academic experience in speech technology, applied ML, or a closely related research field.

Preferred:
The ideal candidate will have at least 3+ years of experience in a formal leadership capacity, including direct management and mentorship of other researchers or scientists. A track record of publications in top-tier conferences or journals is highly desirable.