Audio Software Engineer II # 26-25179

About the role

Contract Duration: 12 Months. Location: Redmond, WA | Sunnyvale, CA - ONSITE. 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.

Interview Process: 2 Rounds [Technical + Behavioral/Discussion on past projects].

About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. For more information, visit www.ustechsolutions.com.

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.

Qualifications

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 non-ML audience, including audio engineers and scientists.
• Experience with audio, speech, or perceptual quality models (e.g. MOS prediction).
• Experience developing lightweight web front ends.

Skills

Tech Stack: Python, PyTorch, Jupyter/Bento, Model-serving and always-on inference capacity, REST/GraphQL-style endpoints, and a lightweight web UI.

Must-Have Hard Skills: Python (PyTorch), ML Concept & Engineering Practices, Audio & Signal Processing.

Good-to-Have Skills: Audio/Speech/Perceptual Quality Models, Audio concepts like waveforms, spectrograms, sample rate.