Senior Nlp/Ml Software Engineer

• Their goal is to break down language barriers worldwide via cutting-edge speech and language AI powering solutions such as video remote interpretation (VRI), over-the-phone interpretation (OPI), interpreter scheduling, simultaneous interpretation, and more.
• They''re seeking a Senior Software Engineer with deep expertise in NLP and ML to help build scalable, production-grade language technology systems.
• This role is ideal for someone passionate about transformer models, audio processing, large-scale data pipelines, and real-world ML deployments.
• This is a high-impact role for someone who wants to shape the future of language accessibility and global communication through sophisticated, scalable ML systems.
• If you''re excited to build at the intersection of language, audio, and AI and do it in a real-world product used globally, we''d love to hear from you.
• Headquartered in Austin, Texas, with remote teams based in San Francisco, Copenhagen, Manila, and Ireland.
• The company has consistently achieved Net Promoter Scores (NPS) above 60, reflecting excellent customer satisfaction.
• The team is collaborative, inclusive, and highly engaged, with strong onboarding and growth support for new hires.
• Responsibilities:Architect, build, and maintain scalable ML-based systems focused on language processing, speech recognition, and real-time communication technologiesTrain and fine-tune transformer-based models (e.
• G.
• , Whisper, wav2vec 2.
• 0, BERT, T5) for tasks such as audio transcription, classification, summarization, and conversational AIDevelop and deploy ML-powered microservices and APIs that integrate tightly with the platform''s cloud infrastructureBuild and manage robust data pipelines for multilingual speech and text datasets, including cleaning, augmentation, and validationCollaborate with cross-functional teams to integrate ML models into production workflows with a strong emphasis on reliability, observability, and user experienceApply software engineering best practices to assure performance, scalability, and maintainability using secure coding principles, automated unit testing, code reviews, horizontal scaling, vertical scaling, microservice architectures, and continuous integration CI/CD pipelinesTroubleshoot, isolate root causes, and provide innovative solutions in reasonable timeframesConduct research to evaluate and adopt emerging ML methods, with a focus on efficient inference,

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