Talantix.io
Stack AV•02.03.26
AI SCORE 8.5

Staff Software Engineer - ML Training Infrastructure (Remote)

$140K–$180K/year
RemoteAI & Machine Learning
Machine Learning•Python•Cloud Technologies•Data Processing•Model Optimization•Software Development

About the Role

We're hiring a Staff Software Engineer - ML Training Infrastructure to join our innovative team at Stack AV. This remote position offers an exciting opportunity to work on cutting-edge AI and machine learning projects that enhance the safety and efficiency of the trucking transportation industry. As a key member of our ML Training team, you will help build a reliable and scalable training framework for our modeling needs.

What You'll Do

  • Develop and maintain ML training infrastructure that supports various modeling teams.
  • Collaborate with ML engineers to create tools for testing, validation, and model understanding.
  • Optimize and deploy ML models to ensure they meet performance standards.
  • Design and implement large data processing pipelines for efficient model training.
  • Build end-to-end ML model pipelines, including logs processing, feature extraction, and model configuration management.

Requirements

  • 5+ years of experience in software engineering with a focus on machine learning.
  • Proven track record of shipping ML products at scale, including NLP, computer vision, or recommender systems.
  • Experience with model training and optimization in the context of autonomous vehicles is a plus.
  • Strong understanding of design tradeoffs and the ability to communicate effectively with cross-functional teams.
  • Familiarity with building scalable and reliable infrastructure in fast-paced environments.

Nice to Have

  • Experience in the trucking or transportation industry.
  • Knowledge of cloud technologies and their application in ML.
  • Prior experience with AV (autonomous vehicles) technologies.

What We Offer

  • Competitive salary range of $140,000 - $180,000 per year.
  • Flexible remote work environment.
  • Opportunities for professional growth and development.
  • Diverse and inclusive workplace culture.
  • Comprehensive health benefits and wellness programs.
Why This Job8.5 of 10

This role offers a unique opportunity to work on cutting-edge AI technologies in a fully remote environment, with a competitive salary and growth potential.

Salary Range
Required
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Optional
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Bonus
0/1

Who Will Succeed Here

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Proficient in Python and experienced with ML frameworks such as TensorFlow or PyTorch for model development and optimization, enabling efficient training infrastructure.

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Strong understanding of cloud technologies like AWS or Azure, with practical experience in deploying ML models and managing cloud resources in a remote work environment.

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Demonstrated experience in data processing and ETL (Extract, Transform, Load) pipelines, with a mindset focused on scalability and reliability in a fast-paced tech environment.

Learning Resources

→Machine Learning Crash Coursecourse
→Python for Data Science Handbookbook
→AWS Machine Learning Documentationguide

Career Path

Staff Software Engineer - ML Training Infrastructure(Now)→Lead Machine Learning Engineer(1-2 years)→Principal Machine Learning Architect(3-5 years)

Market Overview

Market Size 2024
$40B
Annual Growth
38.8%
AI Adoption
75%
Investment
+120%
Labour Demand
+50%
Avg Salary
$150K

Skills & Requirements

Required
Machine LearningPythonCloud Technologies
Growing in Demand
Deep Learning Frameworks (e.g., TensorFlow, PyTorch)MLOps and CI/CD for MLData Engineering and ETL Processes
Declining
Traditional Statistical Analysis (e.g., SPSS, SAS)Rule-Based Expert Systems

Domain Trends

Increased Focus on MLOps
Organizations are investing in MLOps to streamline the ML lifecycle, with 60% of companies prioritizing MLOps in their AI strategies.
Shift Towards Federated Learning
The federated learning market is expected to grow by 40% annually, driven by privacy concerns and the need for decentralized data processing.
Integration of Edge Computing with ML
By 2025, 75% of enterprise-generated data will be processed outside traditional data centers, increasing the demand for ML models that can run on edge devices.

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