What are the responsibilities and job description for the Senior AI/ML Engineer, Audio and NLP position at Theron Partners Inc.?
Job Title: Senior AI/ML Engineer, Audio and NLP
Location: Washington, DC
Duration: Full-time (Direct Hire)
Schedule: Hybrid Role
Job Description:
About the Role:
As a Senior AI/ML Engineer, you will work on audio event detection, speech recognition, transcription, NLP models, and related production pipelines. You should be comfortable taking a capability from data preparation through training, testing, evaluation, deployment support, and performance monitoring.
This role is ideal for an engineer with strong Python experience who understands applied machine learning, model evaluation, and production inference. Experience with lower-level or object-oriented languages such as C or Go is a plus.
Responsibilities:
- Develop, fine-tune, test, and evaluate audio, speech, transcription, and NLP models.
- Work with Whisper or comparable speech models, as well as broader NLP model families.
- Build and maintain data pipelines and inference pipelines for model-driven capabilities.
- Define metrics and evaluation methods for each model, algorithm, or pipeline delivered.
- Support audio event detection, speech detection, transcription, parsing, and related workflows.
- Analyze model quality, latency, robustness, failure modes, and operational limitations.
- Collaborate with backend, data, product, and AI/ML teams to integrate capabilities into the broader platform.
- Document model behavior, evaluation results, limitations, and operational considerations.
Required Qualifications:
- 5 years of professional software engineering, machine learning, data science, or applied AI experience.
- Strong proficiency in Python and modern machine learning frameworks.
- Experience with ASR, audio ML, speech processing, NLP, transcription systems, or related applied ML domains.
- Experience training, fine-tuning, testing, and evaluating machine learning models.
- Experience building data pipelines, inference pipelines, or model-serving workflows.
- Ability to define meaningful metrics and evaluation approaches for model performance and system behavior.
- Strong understanding of data quality, model performance tradeoffs, and production ML practices.