• Identify and correct audio artifacts, loudness inconsistencies, frequency imbalances, and sibilance issues across large-scale voice datasets. • Design and implement scalable audio processing pipelines for voice data • Define and implement scalable audio processing pipelines (EQ, compression, de-essing, dynamic range optimization) and normalization strategies across inter- and intra- voice recordings. • Optimize audio quality across real and synthetic voices to ensure a consistent product experience across multiple use cases. • Lead audio quality decisions during on-site voice actor recording sessions, including microphone selection, placement, gain staging, and environment setup. • Define, document, and enforce audio quality standards for external vendors, including recording setup requirements, signal characteristics, and post-processing expectations, ensuring vendor-produced audio meets Deepgram’s training and product needs even when recordings are not done on-site. • Convert expert-driven, manual audio workflows into automated, repeatable, code-based systems. • Collaborate closely with research to improve training data quality, especially TTS speaker-specific fine-tuning. • Contribute to synthetic data pipelines by defining and validating acoustic characteristics, guiding how different “sound profiles” should be produced and evaluated.

• Apply Lessons Learned From Domain Leaders: Bring practical, everyday lessons learned and experiences from other areas, domains and professions to this problem space. • Engineer Semantic Services: Co-design and build the Service-Oriented Architecture (SOA) components responsible for the lifecycle of semantic data. Your software enables ingestion, abstraction, curation, and publication, versioning, governance & distribution of semantic resources—encompassing formal biomedical ontologies, standard terminologies, and reference lists—integrating them directly into data-in-flight services. • Data Engineering for Continuous Modeling: Develop and deploy ETL pipelines that lift instance-level data into formal vocabularies. You will ensure these pipelines capture the nuanced aspects of the model, including complex relationships, properties, and constraints. • Living Ontology Development: Build services that project formal semantic meaning across the Tetra ecosystem, helping to formalize a "living," real-world ontology that evolves with our data and its usage. • Partner as a Force Multiplier: Collaborate with Scientific Data Engineers, Architects, and Business Leads to integrate semantic artifacts technically. You will provide subject matter expertise, coaching, and training on the governance of controlled vocabularies, ensuring formal semantics and structure are applied consistently across platform applications. • Vocabulary Management: Develop software enabling management of the full lifecycle of our formal vocabularies. You will implement robust systems for versioning, deprecating, and migrating vocabularies across our customer base to ensure seamless operations. • Engineering Mentorship: Serve as the "Informatics SME" for the engineering organization. You will partner with and mentor technical team members—ranging from pure software engineers to scientists—on how to leverage semantic artifacts to provide data with formal definitions, meaning, and context. • Data Exchange Standards: Co-design the "handshake" protocols for data exchange between platform components. You will iteratively develop these standards to ensure that data leaving one system is deterministically understood by another, handling the complex mapping and transformation logic required for true syntactic and semantic interoperability.

• Perform engineering design support within a team environment • Work with other disciplines, project/product stakeholders, partner organizations and suppliers • Develop and maintain models of nuclear plant design and operational capabilities • Analyze regulatory-driven requirements to develop risk-informed solutions • Provide insights and conclusions from analyses with clear communication to peers, management and customers

• Build a scalable web data acquisition platform used across teams. Enable not just your team but other teams to ingest data more safely and efficiently. • Make tradeoffs between costs, reliability and performance in key architecture decisions. • Create shared abstractions and tooling that make it easy to add, maintain, and operate scrapers in production. • Own system reliability, including smart retries, backoff strategies, error handling, and failure recovery. Continuously improve resilience, correctness and maintainability of scraping and data ingestion infrastructure. • Build observability into data pipelines through logging, metrics, and alerting. Monitor data quality. Improve and scale end-to-end data pipelines. • Collaborate cross-functionally to support new data sources and evolving product requirements.