Expert ML Software Engineer

Grvty•Springfield, VA

4d•Onsite

About The Position

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.

Requirements

US Citizen with a TS/SCI Clearance and Ability to obtain a CI Polygraph
Bachelor’s Degree in relevant field of study with 8 years of experience
Expert experience understanding of Kubernetes programming – Python is essential; with experience in Git, Linux, and REST APIs
Expert experience in Backend Development – API design, database integration, containerization (Docker)
Fundamental understanding of Math & Statistics – Linear algebra, probability, and/or calculus for ML theory
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
Expert experience developing ML algorithms — supervised/unsupervised 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)
Expertise in Data Engineering – Data cleaning, feature engineering, preventing data leakage

Nice To Haves

Master’s Degree in relevant field of study with 6 years of experience
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/quantization tradeoffs
Experience with GPU orchestration and scheduling on Kubernetes in shared or multi-tenant clusters — NVIDIA Run:ai, KAI Scheduler, Kueue, Volcano, or Slurm.
Expert experience of Cloud & CI/CD DevOps Pipeline– AWS, Azure, Google Cloud ML services, SageMaker, Vertex AI

Responsibilities

Create and train ML models for classification, regression, forecasting, or deep learning
Build end-to-end ML pipelines for data preprocessing, feature engineering, model training, and evaluation
Deploy models as APIs or backend services using frameworks like FastAPI, Flask, or Django
Track model performance, detect drift, and retrain with new data
Connect ML systems to applications, databases, and cloud platforms.