Machine Learning Engineer#26-02551

Up to $1 per hour

Redmond, WA

Onsite Job Description

Machine Learning Engineer, Perceptual Audio Evaluation

Location:

Fully In-Office, 5days a week at Redmond, WA

Contract Duration:

6 months

Contract Type:

Contingent Worker - W2

Pay Rate:

$70 - $75 per hour on W2

Benefits:

Dental, Health, Vision, 401K, PTO

Note:

This is a W-2-only position. We do not offer visa sponsorship and cannot accept

H-1B, F-1/STEM

OPT, 1099, C2C, or C2H arrangements. Role Overview

We are seeking a Machine Learning Engineer to own and sustain a family of production machine learning models supporting perceptual audio evaluation. This role will be responsible for the end-to-end lifecycle of ML models, including model evaluation, inference services, deployment, monitoring, troubleshooting, and integration into internal tools and workflows. The ideal candidate combines strong Python and PyTorch skills with practical ML engineering experience and foundational knowledge of audio and signal processing. Experience with audio, speech, perceptual quality models, or production ML platforms is highly valued. Top 3 Must-Have HARD Skills

Python +

Deep Learning:

Proficiency in Python and a deep-learning framework such as PyTorch.

Machine Learning +

ML Engineering:

Knowledge of Machine Learning concepts and ML engineering practices, including model evaluation, inference, deployment, troubleshooting, and production ML workflows.

Audio +

Signal Processing:

Basic knowledge of audio and signal processing sufficient to understand and troubleshoot audio-based ML models and their outputs.

Good-to-Have Skills

Experience with audio, speech, or perceptual quality models, such as MOS prediction.

Working familiarity with audio concepts including waveforms, sample rate, and spectrograms, sufficient to sanity-check model outputs.

Experience with the Meta internal ML platform tooling stack or comparable internal ML infrastructure.

Experience deploying and maintaining machine learning models in production.

Experience operating production services, including on-call support, ticket queues, runbooks, access management, and escalation.

Experience developing lightweight web front ends or integrating ML models into internal web-based tools.

Job Responsibilities

Own a family of deep-learning models end to end, including architecture, checkpoints, evaluation pipelines, serving infrastructure, versioning, and failure modes.

Maintain ML models' inference services and evaluation pipelines.

Integrate models into internal and cross-functional tools and workflows through APIs, service endpoints, and lightweight web interfaces.

Operate always-on model inference capacity, including monitoring traffic, resolving throttling issues, tuning auto-scaling, requesting additional capacity, redeploying services, and escalating issues to platform owners as needed.

Run analysis and interpret model evaluation results on request.

Apply minor bug fixes, preprocessing changes, model version updates, and checkpoint swaps.

Support model users and tooling owners across various technical domains, including audio engineers, software engineers, research scientists, and technical program managers.

Troubleshoot production issues across model, data, inference, and service layers.

Maintain operational documentation, runbooks, and procedures for supported ML services.

Serve as on-call support for covered production services.

Partner with engineering, research, and platform teams to improve model reliability, scalability, and usability. 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 and take ownership of technical deliverables.

Ability to troubleshoot and communicate technical issues across engineering and research environments. Preferred Qualifications

Master's or PhD 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. At Tailored Management, we help people get jobs and take pride in their work, so we better take pride in ours too. Not every company has the opportunity to meaningfully and directly impact individuals in a way that makes their lives tangibly better. To better support our employees, we offer a wide variety of benefit options to support you.

We offer:

Medical Coverage - HDHP, PPO, and Surest plan options

Minimum Essential Coverage (MEC)

Dental Insurance

Vision Insurance

Short-Term Disability (STD)

Long-Term Disability (LTD)

Life & AD&D Insurance

Critical Illness Insurance

Accident Insurance

Employee Assistance Program (EAP) Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Los Angeles Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, qualified applicants will be considered for assignment with arrest and conviction records. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness, meet client expectations, standards, and accompanying requirements, and safeguard business operations and company reputation. #TMMT