About The Role
The Machine Learning Engineer will design, build, and operate production ML systems spanning recommendation, classification, forecasting, and natural language applications. The role covers the full model lifecycle: data preparation, experimentation, training, deployment, monitoring, and iterative improvement in cloud environments.
Based in Washington, DC with a remote work arrangement, the role partners with data scientists, platform engineers, and product teams to turn research concepts into reliable services. Success requires balancing model quality with latency, scalability, cost, observability, and maintainability.
Key Responsibilities
- Design, train, and evaluate supervised and deep learning models using Python, PyTorch, TensorFlow, or scikit-learn for production use cases
- Build reproducible data and feature pipelines with SQL, Spark, Airflow, or equivalent workflow orchestration tools
- Deploy and serve models through AWS SageMaker, Vertex AI, Kubernetes, or containerized REST and gRPC services
- Develop model versioning, experiment tracking, and CI/CD workflows using tools such as MLflow, Docker, GitHub Actions, or Terraform
- Monitor production systems for data drift, model degradation, latency, throughput, and infrastructure failures; implement automated alerting and rollback procedures
- Collaborate with data scientists and backend engineers to improve inference performance, testing coverage, and system reliability
- Document model behavior, assumptions, evaluation results, and operational runbooks; contribute to architecture reviews and engineering standards
What We Are Looking For
- 3–8 years of experience in machine learning engineering, applied machine learning, or a closely related software engineering role, including production model deployment
- Strong Python skills and hands-on experience with PyTorch, TensorFlow, scikit-learn, or comparable ML frameworks
- Practical understanding of ML fundamentals, including feature engineering, model selection, cross-validation, calibration, evaluation metrics, and bias-variance tradeoffs
- Experience building data pipelines with SQL and Spark, and working with orchestration or distributed processing systems such as Airflow, Databricks, or equivalent
- Proficiency with cloud infrastructure and production engineering practices, including Docker, Kubernetes, CI/CD, APIs, observability, and infrastructure-as-code
- Bachelor’s or master’s degree in computer science, engineering, statistics, mathematics, or a related technical field
- Bonus: Experience with LLMs, embeddings, RAG systems, model serving optimization, GPU infrastructure, online experimentation, or regulated and privacy-sensitive data