Software Engineer II

SGS Consulting; Washington (District of Columbia); On-site; USD 120,000 - 180,000; Full time

Job summary

SGS Consulting is seeking an experienced ML Engineer to own end-to-end deep-learning models, from architecture to deployment. You will integrate models into internal workflows via APIs and web UI onboarding, and manage on-call operations for always-on inference.

Expert Python, PyTorch, and ML platform tooling are required, with 2+ years deploying models in production. The role demands advanced degrees in a relevant field and knowledge of audio/signal processing, with strong collaboration across

Qualifications

• Proficiency in Python and PyTorch.
• 2+ years deploying ML models in production.
• Experience operating production services, including on-call, runbooks, and escalation.

Responsibilities

• Own a family of deep-learning models end-to-end: architecture, checkpoints, evaluation pipelines and serving infrastructure.
• Integrate models into internal and cross-functional tools via API and UI onboarding.
• Operate always-on model inference: monitor traffic, tune auto-scaling, redeploy when needed.
• Analyze model evaluations, apply minor fixes and manage version bumps.
• Communicate with model users and tooling owners across domains; serve on-call.

Skills

Python

PyTorch

ML engineering

Audio processing

MOS models

ML platform tooling

Education

Master's or PhD in Electrical Engineering / Audio Engineering / Speech or Signal Processing / Acoustics / Computer Science

Bachelor's degree in CS / EE or related

Tools

Internal ML platform tooling

Job description

Job Responsibilities:

Own a family of deep-learning models end to end architecture, checkpoints, evaluation pipelines, serving infrastructure, and failure modesIntegrate 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 escalation to platform owners neededRun analysis and interpret model evaluations on request, apply minor bug fixes and preprocessing changes, and manage version bumps and checkpoint swapsCommunicate 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.

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. 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 Client’s internal ML platform tooling stack.

Education/Experience:

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. 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.