Machine Learning Engineer, Ops

📋 Description

• Design and maintain inference infrastructure for generative audio models.
• Implement and manage high-performance inference engines.
• Orchestrate deployments with Kubernetes and autoscaling.
• Develop and automate CI/CD pipelines for model artifacts.
• Monitor production systems and track latency, resources, performance.
• Collaborate with research teams to optimize serving paths.

🎯 Requirements

• Deep understanding of audio model architectures (TTS/ASR).
• Hands-on Kubernetes (K8S) and autoscaling for production workloads.
• Strong MLOps background with CI/CD and scalable cloud infra.
• Proficiency in Python or Go for tooling and backend services.
• Experience with GPU-accelerated inference and profiling.
• Familiarity with high-performance inference engines (e.g., Triton, vLLM-Omni).

🎁 Benefits

• Competitive salary and equity
• Medical, dental, and vision insurance
• 42 days PTO: 15 PTO, 10 sick, 15 holidays, 2 floating
• Parental leave and fertility support
• 401(k) retirement plan
• Lifestyle spending account: $500/month