Junior Machine Learning Engineer

i3 has an exciting opportunity for a machine learning (ML) engineer to support the development of DoD systems. The ML engineer will develop and program neural network (NN)-based software solutions. Additionally, the ML engineer will aid in the analysis of artificial intelligence (AI)-enabled systems. This position is in Arlington, VA.

Pay Range $93,000 - $210,000

The pay range for this position is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, geographic location, and any applicable laws.

In this role, you will:

• Prototype and implement software-based machine learning systems under the guidance of senior engineers and ML architects.
• Research and evaluate neural network (NN) architectures for specific problems and application requirements.
• Develop, test, and debug Python and C++ software for an NN-based software suite.
• Curate and generate datasets, develop training and evaluation pipelines, and optimize models for accuracy and performance.
• Conduct reproducible experiments and generate technical documentation describing implementations, methodologies, and results.

You should bring:

• Bachelor’s degree in electrical engineering, computer engineering, computer science, mathematics, or a related field.
• 0–2 years of relevant experience, including internships, academic research, or project work.
• Familiarity with neural network architectures and their application to classification problems.
• Programming experience in Python, familiarity with C/C++, and working knowledge of at least one ML framework, such as PyTorch.
• Foundational understanding of linear algebra, probability, and optimization, with strong analytical and problem-solving skills.
• Strong analytical and problem-solving skills, with the ability to learn new tools and technologies.

Preferred: 

• Master’s degree in electrical engineering, computer engineering, computer science or mathematics.
• Experience with training, evaluating, or fine-tuning neural network models.
• Familiarity with profiling ML workloads and identifying performance bottlenecks.
• Exposure to GPU computing, CUDA, tensor cores, or other ML acceleration technologies.
• Familiarity with hardware description languages such as VHDL or Verilog.
• Experience writing technical documentation for software, research, or engineering projects.
• Experience with reverse engineering software, hardware designs, or compiled code to understand system behavior and identify underlying functionality.

Clearance Requirements:

• Ability to obtain a SECRET security clearance.
• U.S. Citizenship required.