Job description

Contract Duration: 12 Months Role Description: 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. Tech Stack: Python, PyTorch, Jupyter/Bento, 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 audio, speech, or perceptual quality models (e.g. MOS prediction) • Experience developing lightweight web front ends Note: • 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 • 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. To know more about US Tech Solutions, please visit www.ustechsolutions.com . US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring. #LI-AS140

Requirements

Must-haves

Contract Duration: 12 Months
Location: Redmond, WA | Sunnyvale, CA - ONSITE Role
Tech Stack: Python, PyTorch, Jupyter/Bento, Model-serving and always-on inference capacity, REST/GraphQL-style endpoints, and a lightweight web UI
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.
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
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
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
Interview Process: 2 Rounds [Technical + Behavioral/Discussion on past projects]
AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring.

Nice-to-haves

Master's or PhD degree in Electrical Engineering, Audio Engineering, Speech or Signal Processing, Acoustics, Computer Science, or a related technical field.
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 audio, speech, or perceptual quality models (e.g. MOS prediction)
Experience developing lightweight web front ends

Application prep

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Keywords to mirror

machine learningmlpythonrestgraphqlapiai

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