ML Engineer
On-siteJacksonville, Florida, United States
Job Summary
Design, build, and deploy production-grade ML systems with end-to-end ownership of the model lifecycle from conception to maintenance. Architect AI-powered solutions enabling natural speech interaction and real-time audio understanding, developing models to extract business-critical insights from unstructured voice data. Build agents operating natively on real-world audio inputs while collaborating with cross-functional teams to shape the AI stack. Handle the entire AI lifecycle, including data acquisition, preprocessing, training, deployment, inference, and monitoring in production environments. Work directly with customers to identify needs and deliver impactful real-world solutions. Participate in continuous improvement of ML infrastructure for scalability and performance.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
- 1-6 years of professional experience in ML engineering
- Strong programming skills in Python
- Hands-on experience with ML frameworks such as PyTorch or TensorFlow
- Familiarity with cloud environments and infrastructure
- Strong understanding of data pipeline design, real-time inference, and model monitoring
- Excellent communication skills with the ability to engage directly with customers and stakeholders
Desired Qualifications
- TypeScript experience
- preferably AWS
- Proven experience building and deploying ML models into production environments
- Demonstrated ability to own the full model lifecycle from data ingestion and model development to deployment and monitoring
- Experience with audio-focused ML projects or similar domains involving unstructured data
- Proficiency in building scalable data pipelines for model training and evaluation
- Familiarity with FastAPI, OpenAI APIs, Baseten, LiteLLM, LiveKit, PostgreSQL, Redis, and S3
- Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices
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