Software Engineer II

Role summary: Own and sustain a family of production machine learning models. Day to day responsibilities include: maintain ML models inference services and evaluation pipelines, integrate models into internal tools, and support the users and tooling owners using models.
Tech stack: Python, PyTorch, Bento (Jupyter-style notebooks), internal model-serving and always-on inference capacity, REST/GraphQL-style endpoints, and a lightweight web UI.
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 as 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.

Minimum Qualifications
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.
Knowledge of Machine Learning concepts and ML engineering practices.
Basic knowledge of audio and signal processing.
Ability to work independently

Preferred Qualifications
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
Working familiarity with audio concepts (waveforms, sample rate*** spectrograms) sufficient to sanity-check model outputs
Excellent communication skills with nonML audience, including audio engineers and scientists.
Experience with internal ML platform tooling stack.
Experience with audio, speech, or perceptual quality models (e.g. MOS prediction)
Experience developing lightweight web front ends