ML Platform Engineer & Data Scientist

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

Job summary

Peraton is seeking a Data Scientist / ML Platform Engineer to contribute across the full ML lifecycle in a HIPAA-governed, FedRAMP-compliant health analytics environment. You will build, train, and evaluate models, and help deploy and monitor them in production.

You will work with ML platforms, orchestration, and data pipelines, collaborating with infrastructure engineers while applying explainability and governance to ML assets.

Qualifications

• 2 years with BS/BA; 0 years with MS/MA; 6 years with HS Diploma/equivalent.
• Experience with SQL and Python and common ML frameworks.
• Hands-on MLFlow or equivalent for experiment tracking and governance.
• Strong SDLC knowledge and GitHub or equivalent.
• Experience with distributed compute (Spark/Databricks) and cloud services.
• Bash/shell scripting for automation; collaboration across teams; ability to obtain Public Trust clearance and US citizenship.

Responsibilities

• Develop, train, and evaluate ML models and contribute to LLM-based capabilities.
• Support model governance and deployment with MLFlow; manage experiments and versioning.
• Contribute to production ML operations: monitoring, drift detection, and alerting.
• Build and improve model serving infrastructure, feature pipelines, and automation.
• Apply explainability techniques and produce technical documentation.
• Support data ingestion, ELT/ETL in Spark, SQL within Snowflake and Databricks.
• Assist with pipeline orchestration, metadata management, and lineage tracking.
• Perform occasional system administration tasks with platform teams.

Skills

SQL

Python

Communication skills

Education

BS/BA in a related field

Tools

MLFlow

GitHub

Spark

Databricks

Bash

scikit-learn

XGBoost

PyTorch

TensorFlow

Databricks REST APIs

Job description

Peraton is seeking a Data Scientist / ML Platform Engineer to contribute across the full ML lifecycle in a HIPAA-governed, FedRAMP-compliant health analytics environment. You will build, train, and evaluate models, and help deploy and monitor them in production.

You will work with ML platforms, orchestration, and data pipelines, collaborating with infrastructure engineers while applying explainability and governance to ML assets.