Machine Learning Engineer

SoftRevery; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

SoftRevery is seeking an ML Engineer to take models from notebook to production and keep them healthy at scale. You will build training and evaluation pipelines, deploy, monitor, and retrain models with a modern MLOps stack. Work with data and business metrics to ship models that measurably move KPIs.

Design experiments, optimize latency and cost, and implement CI/CD for ML systems in the cloud. Proficiency in Python and PyTorch (or TensorFlow) is required, with cloud experience (AWS, GCP or

Qualifications

• 3+ years in ML engineering or applied data science in production.
• Strong Python and PyTorch (or TensorFlow); solid ML fundamentals.
• Hands-on MLOps: pipelines, model registry, monitoring, CI/CD.
• Cloud experience (AWS, GCP, or Azure) and containerization.

Responsibilities

• Build training and evaluation pipelines for ML and generative AI models.
• Deploy, monitor, and retrain models with a modern MLOps stack.
• Design experiments and define offline/online metrics with stakeholders.
• Optimize inference for latency and cost in production.

Skills

Python

PyTorch

TensorFlow

MLOps

Cloud platforms

Tools

Docker

Kubernetes

CI/CD tooling

Model monitoring

Job description

Take models from notebook to production and keep them healthy at scale.

You will build and operate machine-learning systems that hold up in production — training pipelines, evaluation harnesses, and the serving infrastructure around them. You work close to the data and close to the business metric, shipping models that measurably move a KPI rather than sit in a demo.

What you'll do

• Build training and evaluation pipelines for ML and generative AI models.
• Deploy, monitor, and retrain models with a modern MLOps stack.
• Design experiments and define offline/online metrics with stakeholders.
• Optimize inference for latency and cost in production.

What we're looking for

• 3+ years in ML engineering or applied data science in production.
• Strong Python and PyTorch (or TensorFlow); solid ML fundamentals.
• Hands-on MLOps: pipelines, model registry, monitoring, CI/CD.
• Cloud experience (AWS, GCP, or Azure) and containerization.

Nice to have

• LLM fine-tuning, RAG, or agentic systems.
• Experience with feature stores and streaming data.