Principal Engineers, Software

T-Mobile

Remote

Quick summary

Work type: Remote
Location: Bellevue, WA
Salary: $197,227–$227,500 / yr
Posted: 4 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $198k

This role $212k

$130k most similar roles pay here $265k

This role pays more than 63% of similar roles. Most pay $174,600–$220,800 — the shaded band above. At the midpoint, this role pays about $212k versus about $198k for comparable roles.

Based on 239 similar postings.

Employer

About T-Mobile

T-Mobile US is the second-largest wireless carrier in the United States, providing wireless voice, messaging, and data services under the T-Mobile and Metro by T-Mobile brands. Industry: Wireless Telecommunications

T-Mobile currently has 37 open roles on FindRole.

Listed pay typically runs $141,900–$191,100 across 37 roles with salary data.

Most-posted roles

View all roles at T-Mobile

At a glance

TL;DR · Principal Engineers, Software

Role Posting Log in to save

Principal Engineers in Software at T-Mobile’s Bellevue headquarters lead the development of machine learning solutions that predict and prevent network asset failures, working closely with system engineers to deploy scalable ML software. They analyze large datasets using advanced algorithms like Deep Learning and Anomaly Detection, deploying models for real-time monitoring and predictive maintenance. Key responsibilities include designing efficient analytical systems in F#, optimizing neural networks, and integrating ML tools such as ML.Net into production environments. Candidates must have expertise in Scala with Spark for distributed data processing, experience with model tuning techniques, and a strong background in machine learning algorithms. This role demands innovative thinking to solve complex business challenges while ensuring data integrity and reliability across T-Mobile’s expansive network infrastructure.

Skills

Python Scala F# ML.Net Spark Grid search Evolutionary optimization Deep Learning Multilayer Perceptron Recurrent Neural Networks Convolutional Networks Auto Encoders Variational Auto Encoders Bayes Point Machine Random Forest Randomized PCA Anomaly Detection Gradient Boosted Trees Docker

What you'll do

Analyze large amounts of data and build predictive models for T-Mobile network asset failures.
Design and implement machine learning software solutions that scale in highly available environments.
Develop new algorithms and methods to solve challenging business problems using innovative techniques.
Lead the implementation and standardization of toolkits for data science, measurement science, and visualization teams.
Provide senior-level guidance on approaches and methodologies as a subject matter expert in data science.

What we're looking for

7+ years of experience in machine learning and software development or equivalent education
Expertise in deploying ML models for failure prediction using classification or anomaly detection
Proficiency in Scala with Spark for processing large-scale telemetry data on distributed clusters
Strong skills in F# programming to develop high-performance analytical systems
Experience with ML frameworks like ML.Net for model construction and inference
Knowledge of machine learning algorithms including deep learning, neural networks, and random forests
Ability to optimize neural network models through parameter tuning and grid search techniques

Save