Machine Learning Engineer

Tech Ladies®; Raleigh (NC); On-site; USD 120,000 - 170,000; Full time

Job summary

Tech Ladies® in Raleigh is seeking a senior ML Engineer to own design, training, and production deployment of machine learning systems powering core features. You’ll tackle performance, latency, and reliability in production-grade workloads beyond notebooks.

You will work with a senior team to deploy models, build robust data pipelines, and champion LLM-based capabilities. This role demands strong Python skills, cloud ML experience, and a passion for scalable ML systems.

Qualifications

• 3–7 years of experience in ML engineering, applied ML, or related role
• Expert-level Python and hands-on depth in PyTorch, TensorFlow, or scikit-learn
• Production experience with cloud ML platforms and MLOps tooling
• Strong ML fundamentals: feature engineering, evaluation design, regularization, drift
• Experience with containerized deployment (Docker, Kubernetes) and serving systems such as TorchServe/Triton/FastAPI
• MS or BS in Computer Science, Statistics, Engineering, or equivalent practical experience

Responsibilities

• Design, train, and evaluate ML models for production workloads
• Build and maintain feature pipelines in Python and SQL
• Deploy and version models using Docker, Kubernetes, and cloud ML platforms
• Instrument models for monitoring: track drift, latency, and business metrics
• Collaborate with data engineering to build reliable data pipelines using Spark, Airflow
• Introduce and champion LLM-based capabilities with RAG, embeddings, fine-tuning
• Contribute to code reviews, design docs, and ML engineering standards

Skills

Python

ML fundamentals

Communication

Problem solving

Education

MS or BS in CS/Statistics/Engineering

Tools

Docker

Kubernetes

SageMaker

Vertex AI

Azure ML

MLflow

Airflow

TorchServe

Triton

FastAPI

Spark

Job description

Job Description

The role owns the design, training, and production deployment of machine learning systems that directly power core product features - spanning tabular ML, NLP, and increasingly LLM-based workflows.

Working in a small, senior team of ML engineers and data scientists, this person will tackle problems where model performance, inference latency, and reliability all matter - and ship systems that run in production, not just in notebooks.

Key Responsibilities

• Design, train, and evaluate ML models (classification, ranking, NLP, and recommendation use cases) for production workloads
• Build and maintain feature pipelines in Python and SQL, ensuring consistency between training and online serving paths
• Deploy and version models using Docker, Kubernetes, and cloud ML platforms (SageMaker, Vertex AI, or Azure ML), with automated CI/CD for model releases
• Instrument models for monitoring: track drift, latency, and business metrics with alerting and automated retraining triggers
• Collaborate with data engineering to build reliable data pipelines using Spark, Airflow, and modern data stack tooling
• Introduce and champion LLM-based capabilities where they add value - RAG systems, embeddings, fine-tuning - with rigorous evaluation
• Contribute to code reviews, design docs, and the team's ML engineering standards

What We Are Looking For

• 3–7 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple production model deployments
• Expert-level Python and hands‑on depth in at least one major framework: PyTorch, TensorFlow, or scikit-learn
• Production experience with cloud ML platforms and MLOps tooling (MLflow, model registries, CI/CD for ML)
• Strong ML fundamentals: feature engineering, evaluation design, regularization, and handling class imbalance and drift
• Experience with containerized deployment (Docker, Kubernetes) and serving systems such as TorchServe, Triton, or FastAPI
• MS or BS in Computer Science, Statistics, Engineering, or equivalent practical experience

About the Job

Apply before December 10, 2026.

Details

Job type: Full-time

Experience level: Mid-Senior ICs

Location: Raleigh