Software Engineer II

Job Responsibilities:

• Own a family of deep-learning models end to end architecture, checkpoints, evaluation pipelines, serving infrastructure, and failure modes
• Integrate these models into internal and XFN tools and workflows via API/endpoint integration and web UI onboarding.
• Operate always-on model inference capacity: monitor traffic, resolve throttling, tune auto-scaling, request additional capacity, redeploy, and escalate to platform owners needed
• Run analysis and interpret model evaluations on request, apply minor bug fixes and preprocessing changes, and manage version bumps and checkpoint swaps
• Communicate with and support model users and tooling owners across various domains including audio engineers, SDEs, research scientists, TPMs etc.
• Serve as oncall for the covered services.

Skills:

• Proficiency in Python and a deep-learning framework such as PyTorch.
• Knowledge of Machine Learning concepts and ML engineering practices.
• Basic knowledge of audio and signal processing.
• Experience with audio, speech, or perceptual quality models (e.g. MOS prediction)
• Working familiarity with audio concepts (waveforms, sample rate, spectrograms) sufficient to sanity-check model outputs
• Experience with Client’s internal ML platform tooling stack.

Education/Experience:

• Master's or PhD degree in Electrical Engineering, Audio Engineering, Speech or Signal Processing, Acoustics, Computer Science, or a related technical field.
• 2+ years of hands-on experience deploying and maintaining machine learning models in production. Experience operating production services, including oncall, ticket queues,
• runbooks, access management, and escalation
• Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience.
• Proficiency in Python and a deep-learning framework such as PyTorch