Machine Learning Engineer

Full Scope; Fort Meade (MD); On-site; USD 120,000 - 180,000; Full time

Job summary

Full Scope in Fort Meade, MD seeks a Machine Learning Engineer to design, build, and deploy ML models and pipelines. You will collaborate with data scientists, engineers, and product managers to translate business needs into scalable AI solutions.

The role requires strong programming in Python/R/Java and hands-on experience with TensorFlow, PyTorch, and cloud deployment. You will preprocess data, optimize models for accuracy and speed, and contribute to a robust ML infrastructure while staying

Qualifications

• Bachelor’s or Master’s degree in CS/Engineering/Math; PhD is a plus.
• Proven ML Engineer experience with Python, R or Java.
• Hands-on with ML frameworks TensorFlow/PyTorch/Scikit-learn.
• Data processing with Pandas/NumPy; visualization with Matplotlib/Seaborn.
• Experience deploying ML on cloud platforms (AWS/GCP/Azure).

Responsibilities

• Design, develop, and implement ML models and algorithms.
• Collaborate with cross-functional teams to translate requirements.
• Perform data analysis and preprocessing for model training.
• Optimize models for performance, accuracy, and scalability.
• Deploy models to production and monitor performance.
• Develop ML pipelines and infrastructure.
• Stay current with ML/AI research and advancements.
• Participate in code reviews, teams, and documentation.

Skills

Python

R

Java

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

Matplotlib

Seaborn

Cloud platforms

Git

Docker

Kubernetes

NLP

Computer vision

Hadoop

Spark

Kafka

Data visualization

Teamwork

Communication

Education

Bachelor’s or Master’s degree in CS/Engineering/Math

PhD is a plus

Tools

Docker

Kubernetes

Git

Jupyter

AWS

GCP

Azure

Job description

Job Title:Machine Learning Engineer

Location:Fort Meade, MD

Required Clearance: TS/SCI w/ Full-Scope Poly

Salary:Competitive

We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning, data science, and software engineering. You will work closely with data scientists, engineers, and product managers to design, develop, and deploy machine learning models and solutions that drive business value.

Key Responsibilities

• Design, develop, and implement machine learning models and algorithms to solve real-world problems.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Conduct data analysis and preprocessing to ensure high-quality data for model training.
• Optimize and fine-tune models for performance, accuracy, and scalability.
• Deploy machine learning models into production and monitor their performance.
• Develop and maintain machine learning pipelines and infrastructure.
• Stay current with the latest research and advancements in machine learning and AI.
• Participate in code reviews, team meetings, and contribute to a collaborative development environment.
• Document processes, models, and findings comprehensively.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
• Proven experience as a Machine Learning Engineer or in a similar role.
• Strong proficiency in programming languages such as Python, R, or Java.
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
• Solid understanding of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning.
• Experience with data processing tools like Pandas, NumPy, and data visualization tools such as Matplotlib or Seaborn.
• Familiarity with cloud platforms like AWS, Google Cloud, or Azure for model deployment and scaling.
• Strong problem-solving skills and the ability to think critically and analytically.
• Excellent communication and teamwork skills.

Preferred Qualifications

• Experience with natural language processing (NLP) and computer vision.
• Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
• Knowledge of software development best practices and version control systems like Git.
• Experience with containerization tools like Docker and orchestration tools like Kubernetes.
• Previous experience in a fast-paced, startup environment.