Job Description:
The engineer in this role will join a multidisciplinary R&D team dedicated to researching, optimizing, and deploying high-performance machine learning models on target hardware for intelligence, defense, and biomedical applications. This engineer will be responsible for designing and implementing discriminative and generative models for computer vision and signal processing, leveraging low-level software and hardware expertise to accelerate performance across diverse hardware environments, including edge devices, desktop workstations, and multi-GPU clusters.
The successful candidate will contribute at all stages of the model development lifecycle, from ideation to deployment, for tasks such as object detection, anomaly detection, semantic segmentation, forecasting, and synthetic data generation. They will work with other engineers and subject matter experts to conduct exploratory data analysis, data preprocessing, model implementation, and rigorous model evaluation. Emphasis will be placed on optimizing models to meet strict latency, throughput, and power constraints, and in deploying models efficiently to targeted hardware platforms.
Candidate must be a US Citizen and possess an Active TS/SCI security clearance.
Location: This role is based in the Dayton, OH area, and is 100% onsite.
Basic Qualifications:
· Candidate must be a U.S. Citizen and possess (or be eligible to obtain) and maintain a SECRET Clearance. · Bachelor's in Engineering, Computer Science, Math/Statistics, Physics, or related STEM discipline; Master's or Ph.D. preferred. · Strong programming proficiency in Python and C/C++, with proven experience in writing and optimizing low-level code for high-performance computing on embedded and edge hardware. · Understanding of machine learning methodologies for classification, regression, clustering, dimensionality reduction, and generative modeling, with experience selecting and optimizing algorithms for real-time applications. · Proficiency with machine learning frameworks such as PyTorch or TensorFlow/Keras, with experience in extending or customizing framework components for specialized hardware. · Experience with data manipulation and visualization tools, such as pandas, scikit-learn, and plotly, with a strong grasp of data preprocessing for complex datasets. · Strong experience with version control systems such as Git and familiarity with best practices in collaborative code management and code review.
Desired Qualifications:
· Extensive experience with embedded systems hardware and software, including working knowledge of assembly-level programming or low-level language optimization. · Proven track record deploying machine learning models in production environments, particularly on constrained devices or distributed computing platforms. · Deep expertise with CUDA and/or OpenCL for accelerating compute-intensive tasks and optimizing ML model performance on GPU-based systems. · Fluency with shared Linux clusters and HPC workstations, with experience configuring and optimizing these environments for distributed model training and inference. · Demonstrated experience leading collaborative R&D or engineering efforts, with a history of delivering optimized ML solutions that meet customer requirements in demanding, real-world applications.