6 days ago
Fairborn, OH, USASenior
Base Salary
$93k - $116k/yr
Responsibilities
- Lead the technical approach for AI/ML tasks from problem formulation and data strategy through model development, evaluation, and transition.
- Develop dataset strategies using synthetic data, simulation-to-real transfer, active learning, and weak supervision.
- Own model architecture selection, training, fine-tuning, distributed training, hyperparameter strategy, and error analysis.
- Build rigorous evaluations using task-grounded metrics, statistical comparisons, ablations, adversarial tests, out-of-distribution sets, and failure-mode analysis.
- Lead generative AI efforts involving model selection, supervised and preference-based fine-tuning, RAG, agentic systems, tool use, and guardrails.
- Improve model efficiency and deployment readiness through quantization, distillation, pruning, export, and runtime optimization.
- Establish reproducibility through experiment tracking, data and model versioning, containerized environments, and CI for ML code.
- Mentor and technically guide less experienced engineers while raising team rigor.
- Contribute to proposals, white papers, R&D pitches, and customer presentations.
- Partner with algorithm, software, and program teams to transition models into real-time and embedded applications.
Requirements
- Bachelor's degree in computer science, engineering, mathematics, physics, statistics, or a related technical discipline plus 4+ years of relevant experience, or a master's degree plus 2+ years.
- Demonstrated experience taking ML systems from concept to working capability rather than only developing prototype notebooks.
- Strong Python proficiency and deep PyTorch expertise, including custom training loops, datasets and dataloaders, mixed precision, and profiling.
- Experience with Hugging Face Transformers, PEFT, TRL or equivalent, and model serving or inference frameworks such as vLLM, TGI, or Ollama.
- Substantive experience in generative AI and LLM fine-tuning, deep learning for perception or signal domains, or reinforcement learning.
- Strong evaluation methodology and statistical literacy, including confidence intervals and significance testing.
- Experience mentoring or technically leading engineers and communicating technical limitations to program managers, customers, and executives.
- Preferred qualifications include distributed training, large-scale data pipelines, edge deployment, multimodal models, sensor-data ML, production MLOps, defense mission knowledge, publications or open-source contributions, AI assurance, an active DoD security clearance, and an MS or PhD.
Benefits
- At least four weeks of paid time off to start.
- 10% employer retirement contribution.
- 100% tuition support.
- Comprehensive and competitive benefits package.
- The position is based in Dayton, Ohio.
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