Software Engineer II - Machine Learning Engineer, Perceptual Audio Evaluation

Spectraforce Technologies, Inc.; Redmond (WA); On-site; USD 120,000 - 180,000; Full time

Job summary

Spectraforce Technologies, Inc. is seeking a Software Engineer II - Machine Learning Engineer, Perceptual Audio Evaluation in Redmond, WA.

The role focuses on owning a family of production ML models, maintaining inference services and evaluation pipelines, and integrating models into internal tools and web UI. You will work with Python, PyTorch, Bento notebooks, and internal model-serving platforms to keep models running reliably, with on-call responsibilities and collaboration across ML

Qualifications

• Bachelor's degree in CS, EE, or related field or equivalent practical experience.
• Proficiency in Python and PyTorch.
• Knowledge of ML concepts and ML engineering practices.
• Basic knowledge of audio and signal processing.

Responsibilities

• Own end-to-end DL model family: architecture, checkpoints, eval, serving, and failure modes
• Integrate models into internal tools via API/endpoint and web UI onboarding
• Operate always-on inference: monitor traffic, scale, redeploy, escalate as needed
• Analyze model evaluations and perform minor bug fixes
• Support model users and tooling owners across domains including audio engineers and SDEs

Skills

Python

PyTorch

ML concepts

Audio basics

Education

Bachelor's degree in CS/EE or related field

Tools

Model-serving platform

REST/GraphQL APIs

Job description

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

1. Proficiency in Python and a deep-learning framework such as PyTorch.
2. Knowledge of Machine Learning concepts and ML engineering practices.
3. 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 elevate 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