Machine Learning Engineer
On-siteShelton, Connecticut, United States
Job Summary
Take models and analyses from prototype to production, owning them once deployed. Build and maintain data pipelines feeding large-scale rail inspection datasets, including ultrasonic and electromagnetic sources. Implement model and data versioning to ensure full traceability, while monitoring for drift and degradation to trigger retraining paths. Design and implement APIs and services delivering model output to consumers, and orchestrate training and inference workloads on AWS. Set team engineering standards for testing, code review, CI/CD, and releases. Automate manual steps between ideas and running models, writing clean, tested code following best practices. Collaborate with cloud and UK engineering teams to define boundaries and manage shared data sources.
Required Qualifications
- Strong proficiency in Python, including the scientific stack (NumPy, Pandas, Scikit-learn, or similar)
- Experience putting machine learning models or statistical analyses into production and keeping them running
- Experience building data pipelines and working with structured and unstructured data at scale
- Solid understanding of SQL and relational and non-relational databases
- Experience with AWS cloud services and cloud-native architecture
- Practical experience with containerization (Docker) and infrastructure-as-code
- Understanding of software engineering principles: testing, code quality, design patterns
- Familiarity with version control (Git), CI/CD pipelines, and agile development practices
- Strong problem-solving skills and ability to learn new technologies quickly
- Good communication skills - able to explain technical concepts to non-technical stakeholders
- A collaborative, team-first mindset aligned with our values of being Humble, Hungry, and Smart
Desired Qualifications
- Bachelor's degree in computer science, engineering, or a related technical field
- MLOps tooling: MLflow, SageMaker Pipelines, Kubeflow, DVC, Weights & Biases, or similar
- Workflow orchestration (Airflow, Dagster, Prefect, Step Functions)
- Observability and monitoring tooling (CloudWatch, Grafana, Datadog, or similar)
- Experience being the first engineer on a data science team
- Signal processing or work with sensor data
- Experience in rail testing, NDT, or sensor-based inspection industries (ultrasound, eddy current, electromagnetic, etc.)
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