Junior Machine Learning Engineer
Integration Innovation, Inc.
Arlington, VA0–2 yrs1h agovia LinkedIn
This job at a glance
- Who can apply
- New grads and up to 2 years · Bachelor's degree
- Must know
- PythonC++CPyTorch
- Nice to have
- Machine learningDeep learning
- Work
- On-site in Arlington
- Pay
- Not stated by the employer.
- Skill demand
- Python80%PyTorch39%C++16%C2%of 99 similar entry-level jobs posted in the US this week
- Listed
- Nov 2, 2026 · still open
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About the Role
Integration Innovation, Inc. (i3) seeks a machine learning (ML) engineer to support the development of DoD systems. This role involves developing and programming neural network (NN)-based software solutions and aiding in the analysis of artificial intelligence (AI)-enabled systems.
Location: Arlington, VA
Key Responsibilities
- 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.
Requirements & Tech Stack
- 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.
- Ability to learn new tools and technologies.
Preferred Qualifications
- 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.
Clearance Requirements
- Ability to obtain a SECRET security clearance.
- U.S. Citizenship required.