Senior AI/ML Engineer

SRC•Fairborn, OH

About The Position

SRC, Inc. is seeking a Senior AI/ML Engineer to lead technical execution of machine learning and generative AI work supporting ISR, electronic warfare, advanced sensing, and decision science missions. The role involves taking ambiguous mission needs from problem formulation and data strategy through model development, evaluation, and transition toward program use. Work spans classical machine learning, deep learning, reinforcement learning, and generative AI, with the engineer expected to select appropriate methods and provide defensible evidence of performance. The engineer will lead tasks of moderate scope, guide less experienced engineers, contribute to proposals and customer-facing technical materials, and establish rigorous experimental practices. The position emphasizes experience transitioning models beyond prototypes, understanding the gap between benchmark and field performance, and working with algorithm engineers, software engineers, and program staff to move models into real-time and embedded applications. The role is based in Fairborn, Ohio, with an anticipated salary range of $93,000 to $116,000 annually and 10% travel.

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, Statistics, or a related technical discipline, plus at least 4 years of relevant experience; or a Master's degree plus at least 2 years of relevant experience.
Demonstrated record of taking ML systems from concept to working capability, beyond prototype notebooks.
Strong proficiency in Python and deep expertise in PyTorch or an equivalent framework, including custom training loops, datasets and dataloaders, mixed precision, and profiling.
Practical experience with the modern LLM toolchain, including Hugging Face Transformers, PEFT, TRL or equivalents, and serving or inference frameworks such as vLLM, TGI, or Ollama.
Substantive experience in at least one of generative AI and LLM fine-tuning, deep learning for perception or signal domains, or reinforcement learning.
Rigorous approach to evaluation and statistical literacy, including confidence intervals, significance, and distinguishing real improvements from noise.
Experience mentoring or technically leading other engineers.
Ability to explain technical work and its limitations to program managers, customers, and executives.
Excellent written and verbal communication.
Must meet security clearance eligibility requirements.

Nice To Haves

Experience with distributed training, such as FSDP, DeepSpeed, or Ray, and large-scale data pipelines.
Experience deploying models to constrained or edge environments using ONNX, TensorRT, TorchScript, quantized inference, or embedded GPU/SoC targets.
Multimodal model experience, including vision-language models, RF/sensor-conditioned models, or cross-modal retrieval.
Experience applying ML to radar, SAR, RF, EO/IR, acoustic, or other sensor data, and familiarity with the relevant phenomenology.
Production MLOps experience with tools or practices such as Kubeflow, MLflow model registry, feature stores, monitoring, and drift detection.
Familiarity with DoD mission domains, technology transition pathways, and test and evaluation for autonomous or ML-enabled systems.
Publications, open-source contributions, or competition results demonstrating technical depth.
Understanding of AI assurance topics, including model cards, red-teaming, uncertainty quantification, and responsible AI practice in a defense context.
Active DoD security clearance.
Master's or Ph.D. in a related technical discipline.

Responsibilities

Lead the technical approach for AI/ML tasks of moderate scope, from problem formulation and data strategy through development, evaluation, and transition.
Design dataset strategies for domains with scarce or costly labeled data, including synthetic data generation, simulation-to-real transfer, active learning, and weak supervision.
Own end-to-end model development, including method and architecture selection, training, fine-tuning, distributed training when appropriate, hyperparameter strategy, and error analysis.
Develop rigorous evaluation methodologies using task-grounded metrics, statistically sound comparisons, ablations, adversarial and out-of-distribution test sets, and clear characterization of failure modes.
Lead generative AI work, including model selection, supervised and preference-based fine-tuning, RAG architecture, agentic and tool-using systems, and guardrail design.
Apply reinforcement learning when appropriate and explain when it is not appropriate.
Improve model efficiency and deployment readiness through quantization, distillation, pruning, export, runtime optimization, and consideration of compute, latency, and accuracy tradeoffs.
Establish and enforce reproducibility practices, including experiment tracking, data and model versioning, containerized environments, and CI for ML code.
Guide, review, and develop the technical and research work of less experienced engineers.
Contribute to proposals, white papers, and internal R&D pitches, and help present findings and recommendations to customers.
Partner with algorithm engineers, software engineers, and program staff to migrate models from research code into real-time and embedded applications.
Identify technical risks early and propose alternative approaches.