Senior NLP/ML Software Engineer

Our US-based client is a fast-growing, mission-driven language technology company developing the next generation of interpretation and multilingual communication platforms. 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 technologies
• Train and fine-tune transformer-based models (e.g., Whisper, wav2vec 2.0, BERT, T5) for tasks such as audio transcription, classification, summarization, and conversational AI
• Develop and deploy ML-powered microservices and APIs that integrate tightly with the platform's cloud infrastructure
• Build and manage robust data pipelines for multilingual speech and text datasets, including cleaning, augmentation, and validation
• Collaborate with cross-functional teams to integrate ML models into production workflows with a strong emphasis on reliability, observability, and user experience
• Apply 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 pipelines
• Troubleshoot, isolate root causes, and provide innovative solutions in reasonable timeframes
• Conduct research to evaluate and adopt emerging ML methods, with a focus on efficient inference, transfer learning, and low-latency deployment
• Stay up to date with industry trends, emerging technologies, and best practices in software development and AI

REQUIRED EXPERIENCE:

• 5+ years of hands-on software development experience using Python, Node.js, and TypeScript
• Agile development experience and strong communication skills in English (written and verbal)
• Solid background in machine learning, with experience in supervised and unsupervised algorithms
• Practical experience with transformers, generative models, and LLMs in NLP or audio processing
• Familiarity with Hugging Face Transformers, TorchAudio, OpenAI APIs, or similar platforms
• Expertise in developing REST APIs and microservices in production environments
• Deep understanding of containerization and orchestration using Docker and Kubernetes
• Strong knowledge of AWS (S3, ECS, SageMaker, CloudWatch, Load Balancer Controller, etc.)
• Experience with NoSQL and RDBMS databases
• Proficient in Git, Jira, Infrastructure as Code (IaC) tools, and CI/CD pipelines
• Strong problem-solving skills and ability to work independently or in a team

PREFERRED (But NOT Required):

• Bachelor's degree in Computer Science, Engineering, or a related technical field
• Experience building ML-based products for interpretation, transcription, or conversational interfaces
• Familiarity with Pulumi, GCP, or Azure as alternative cloud environments
• Knowledge of innovation accelerators or previous work with AI startup toolkits
• Familiarity with real-time data streaming and low-latency model serving