Machine Learning Engineer

Jobtailor; California (MO); On-site; USD 130,000 - 190,000; Full time

Job summary

Jobtailor is seeking a senior data/ML engineer to design and operate data pipelines for unstructured logs, enabling real-time network insights across complex environments.

You will build synthetic data, optimize ML workflows and tooling, and collaborate with cross-functional teams to deliver scalable infrastructure, robust analytics, and reliable data quality.

Qualifications

• Bachelor's degree in STEM with 5+ years of relevant experience
• Master's degree in STEM with 3+ years of relevant experience
• PhD in STEM with 0+ years of relevant experience or equivalent experience
• 5+ years of experience in data engineering, machine learning engineering, or related roles
• Data Pipeline experience: ingesting, cleansing, auditing unstructured/semi-structured data
• ML Infrastructure experience: dataset creation, labeling, evaluation
• Python and data processing frameworks (e.g., Spark, Beam, Ray)
• ML systems/tools: training pipelines, model evaluation frameworks
• Human-in-the-loop ML or active learning experience preferred
• Exposure to large language models, CV, or speech datasets preferred
• Experience building internal tools for annotation/operations teams preferred

Responsibilities

• Build dynamic troubleshooting agents for networks
• Solve unstructured production log data complexities
• Optimize hardware utilization for data collection
• Automate synthetic datasets creation
• Architect data infrastructure for real-time network failure analysis
• Design and scale automated pipelines transforming raw production logs into insights
• Develop systems generating synthetic data for edge cases learning
• Tackle unique network complexity problems
• Optimize data collection and hardware utilization

Skills

Data Engineering

Machine Learning Engineering

Data Pipeline

Data Processing

Synthetic Data Creation

Real-Time Analysis

Network Troubleshooting

Data Cleansing

Model Evaluation

Active Learning

Education

Bachelor's degree in STEM

Master's degree in STEM

PhD in STEM

Tools

Python

Spark

Beam

Ray

ML pipelines

Model evaluation frameworks

Job description

Responsibilities

• build dynamic troubleshooting agents that understand networks
• solve unstructured production log data complexities
• optimize hardware utilization for data collection
• automate synthetic datasets creation
• architect data infrastructure for real-time network failures analysis
• design and scale automated pipelines transforming raw production logs into insights
• develop systems generating synthetic data for edge cases learning
• tackle unique network complexity problems
• optimize data collection and hardware utilization

Requirements

• Bachelor's degree in STEM and 5+ years of relevant experience
• Master's degree in STEM and 3+ years of relevant experience
• PhD in STEM +0 years of relevant experience or equivalent related work experience
• 5+ years of experience in data engineering, machine learning engineering, or related roles
• Data Pipeline experience, designing and scaling data pipelines for unstructured or semi-structured data, including ingestion, cleansing, and auditing
• ML Infrastructure experience working with ML data workflows, including dataset creation, labeling, and evaluation
• Experience with Python and data processing frameworks (e.g., Spark, Beam, Ray)
• Experience with ML systems and tools, such as training pipelines and model evaluation frameworks
• Experience with human-in-the-loop ML systems, active learning, weak supervision or self-evolving agents (preferred)
• Exposure large language models, computer vision, or speech datasets (preferred)
• Experience building internal tools or platforms used by annotation or operations teams (preferred)

Hard Skills

• Data Engineering
• Machine Learning Engineering
• Data Pipeline
• Data Processing
• Synthetic Data Creation
• Real-Time Analysis
• Network Troubleshooting
• Data Cleansing
• Model Evaluation
• Active Learning