Software Engineer II

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 escalation 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.

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.

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.