Machine Learning Engineer II, Perceptual Audio Evaluation
Posted: 10/02/2026
Job Number: 42861
Job Description
Machine Learning Engineer II, Perceptual Audio Evaluation
Onsite
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
THE PROMISES WE MAKE:
At Crystal Equation, we empower people and advance technology initiatives by building trust. Your recruiter will prep you for the interview, obtain feedback, guide you through any necessary paperwork and provide everything you need for a successful start. We will serve to empower you along the way and provide the path for your professional journey.
For more information regarding our Privacy Policy, please visit crystalequation.com/privacy.
Onsite
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.
- 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
- 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
THE PROMISES WE MAKE:
At Crystal Equation, we empower people and advance technology initiatives by building trust. Your recruiter will prep you for the interview, obtain feedback, guide you through any necessary paperwork and provide everything you need for a successful start. We will serve to empower you along the way and provide the path for your professional journey.
For more information regarding our Privacy Policy, please visit crystalequation.com/privacy.
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