Job Title: Software Engineer II - Machine Learning Engineer, Perceptual Audio Evaluation
Location: Redmond, WA - 5 days onsite (Sunnyvale can be considered)
Duration: 9+ Months
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), Meta internal model-serving and always-on inference capacity, REST/GraphQL-style endpoints, and a lightweight web UI.
Top 3 Must-Have HARD 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.
Good to Have Skills:
- 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 Meta internal ML platform tooling stack.
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 Meta internal ML platform tooling stack.
- Experience with audio, speech, or perceptual quality models (e.g. MOS prediction)
- Experience developing lightweight web front ends
Interviews:
- Behavioral - share past work experiences
- Technical
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Spectraforce Technologies
Oct 03, 2026