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ML Software Engineer","expert-ml-software-engineer-z46xtfkg","https:\u002F\u002Fjob-boards.greenhouse.io\u002Fgrvty\u002Fjobs\u002F4372498009","TS\u002FSCI",[409],"Information Technology",[411],"Springfield, VA","\u003Cp>\n    \u003Cstrong>\n        What Impact You'll Have\n    \u003C\u002Fstrong>\n\u003C\u002Fp>\n\u003Cp>\n    We are searching for a machine learning (ML) engineer in support of our customer in Springfield, VA. This ML Engineering role is onsite and combines software engineering and machine learning expertise. You will design, build, and maintain ML systems that learn from data to automate decision-making, such as predictive models, recommendation engines, or anomaly detection systems.\n\u003C\u002Fp>\n\u003Cp>\n    \u003Cstrong>\n        What You'll be Owning\n    \u003C\u002Fstrong>\n\u003C\u002Fp>\n\u003Cul>\n    \u003Cli>\n        Model Development: Create and train ML models for classification, regression, forecasting, or deep learning\n    \u003C\u002Fli>\n    \u003Cli>\n        Pipeline Design: Build end-to-end ML pipelines for data preprocessing, feature engineering, model training, and evaluation\n    \u003C\u002Fli>\n    \u003Cli>\n        Deployment: Deploy models as APIs or backend services using frameworks like FastAPI, Flask, or Django\n    \u003C\u002Fli>\n    \u003Cli>\n        Monitoring & Maintenance: Track model performance, detect drift, and retrain with new data\n    \u003C\u002Fli>\n    \u003Cli>\n        Integration: Connect ML systems to applications, databases, and cloud platforms.\n    \u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\n    \u003Cstrong>\n        What You Must Have\n    \u003C\u002Fstrong>\n\u003C\u002Fp>\n\u003Cul>\n    \u003Cli>\n        US Citizen with a TS\u002FSCI Clearance and Ability to obtain a CI Polygraph\n    \u003C\u002Fli>\n    \u003Cli>\n        Bachelor’s Degree in relevant field of study with 8 years of experience\n    \u003C\u002Fli>\n    \u003Cli>\n        Expert experience understanding of Kubernetes programming – Python is essential; with experience in Git, Linux, and REST APIs\n    \u003C\u002Fli>\n    \u003Cli>\n        Expert experience in Backend Development – API design, database integration, containerization (Docker)\n    \u003C\u002Fli>\n    \u003Cli>\n        Fundamental understanding of Math & Statistics – Linear algebra, probability, and\u002For calculus for ML theory\n    \u003C\u002Fli>\n    \u003Cli>\n        Experience with MLOps tooling and workflow orchestration — experiment tracking and model registry (MLflow or equivalent), and pipeline orchestration on Kubernetes (Kubeflow, Argo Workflows, or Airflow) — supporting automated retraining, model versioning, and drift detection\n    \u003C\u002Fli>\n    \u003Cli>\n        Expert experience developing ML algorithms — supervised\u002Funsupervised learning, neural networks, and NLP — with hands-on expertise in at least one major framework (PyTorch or TensorFlow) and the broader Python ML stack (scikit-learn, pandas, NumPy)\n    \u003C\u002Fli>\n    \u003Cli>\n        Expertise in Data Engineering – Data cleaning, feature engineering, preventing data leakage\n    \u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\n    \u003Cstrong>\n        What Would be Nice to Have\n    \u003C\u002Fstrong>\n\u003C\u002Fp>\n\u003Cul>\n    \u003Cli>\n        Master’s Degree in relevant field of study with 6 years of experience\n    \u003C\u002Fli>\n    \u003Cli>\n        Experience serving models for low-latency inference at scale, including LLM serving frameworks (vLLM, TGI, or NVIDIA Triton Inference Server), GPU memory management, and batching\u002Fquantization tradeoffs\n    \u003C\u002Fli>\n    \u003Cli>\n        Experience with GPU orchestration and scheduling on Kubernetes in shared or multi-tenant clusters — NVIDIA Run:ai, KAI Scheduler, Kueue, Volcano, or Slurm.\n    \u003C\u002Fli>\n    \u003Cli>\n        Expert experience of Cloud & CI\u002FCD DevOps Pipeline– AWS, Azure, Google Cloud ML services, SageMaker, Vertex AI\n    \u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\n    \u003Cstrong>\n        Pay Range:\n    \u003C\u002Fstrong>\n    At GRVTY, we understand that compensation is influenced by many factors—such as geographic location, federal contract labor categories, wage rates, prior experience, skillsets, education, and certifications.\nWe’re proud to offer a work environment that empowers our team to achieve a strong work-life balance. GRVTY provides competitive pay, comprehensive benefits, and meaningful opportunities for professional growth.\nOur benefits package is designed to support the well-being of our employees and their families, and includes coverage in areas such as healthcare, financial wellness, retirement planning, family assistance, continued education, and paid time off.\n\u003C\u002Fp>\n\u003Cul>\n    \u003Cli>\n        The pay range for this position is: $150,000 - $165,000\n    \u003C\u002Fli>\n\u003C\u002Ful>\n"]