Machine Learning Engineer

Sperry Rail, Inc.; Shelton (CT); On-site; USD 120,000 - 180,000; Full time

Job summary

Sperry Rail, Inc. is seeking a Machine Learning Engineer to join our US data science team in Shelton, CT.

You will turn prototype models into production-grade systems, building scalable data pipelines, versioning data and models, and deploying on AWS with SageMaker and related tools. You will own end-to-end model lifecycle, monitor for drift, and collaborate with cloud and UK teams to ensure robust, reproducible results across rail inspection data.

Qualifications

• Strong proficiency in Python and the scientific stack.
• Experience putting machine learning models into production and keeping them running.
• Experience building data pipelines and working with structured and unstructured data at scale.
• SQL and relational/non-relational databases.
• Experience with AWS cloud services and cloud-native architecture.
• Containerization (Docker) and infrastructure-as-code.
• Software engineering principles: testing, code quality, patterns.
• Version control (Git), CI/CD, and agile practices.
• Strong problem-solving and quick learning abilities.
• Good communication to explain technical concepts to non-technical stakeholders.
• Bachelor's degree in CS/engineering or related fields.
• ML tooling and workflow orchestration: MLflow, SageMaker Pipelines, Kubeflow, etc.
• Observability and monitoring tooling familiarity.

Responsibilities

• Take models and analyses from prototype to production, owning them thereafter.
• Build and maintain data pipelines feeding models across large-scale rail inspection data.
• Implement model and data versioning to trace results to code and data.
• Monitor deployed models for drift, degradation, and data quality; design retraining paths.
• Build and maintain APIs/services delivering model outputs to users and systems.
• Design compute/orchestration for training/inference on AWS (S3, Lambda, Glue, SageMaker).
• Set engineering standards: testing, code review, environments, CI/CD, release practices.
• Coordinate with cloud engineering and UK teams on data sources and platform.
• Automate manual steps between ideas and running models.
• Write clean, tested, well-documented code following best practices.
• Participate in code reviews, sprint planning, and technical design discussions.
• Document architecture decisions, runbooks, and operational procedures.

Skills

Python

ML in production

Data pipelines

SQL

AWS

Docker

Git

CI/CD

Agile

Communication

Education

Bachelor's degree in computer science, engineering, or a related technical field

Tools

MLflow

SageMaker

Kubeflow

Airflow

Dagster

Step Functions

CloudWatch

Grafana

Job description

Machine Learning Engineer

Sperry Rail, Inc. Shelton, Connecticut, United States

About this position

About Sperry:

Sperry Rail is on a mission-critical journey to revolutionize the Rail Flaw Detection industry. Through the continuous development of cutting-edge diagnostic technologies and AI-assisted analysis, we are transforming railway safety worldwide. Our global engineering teams work collaboratively to develop step-change technologies that define Sperry as the unparalleled market leader.

For nearly a century, we have repeatedly modernized and improved rail diagnostics through our relentless pursuit of improvement. Determined is an understatement. We are obsessed with advancing science and raising the bar on what’s possible with our ever-improving suite of products and service offerings.

Emboldened through the shared values of honesty, accountability, passion, integrity, and teamwork, we are driven by the challenge and bridging concepts with fruition. Each technologist entering Sperry imprints themselves into our brand and further galvanizes a culture of innovation and advancement. Allow us to be clear, Thought Leaders are welcome!

We are agile and hungry and invite those with similar passions to join us in challenging the status quo and bringing new ideas to the market. Fast-paced, high-touch with a distinct sense of purpose. We offer more than a job; we offer an opportunity to be part of something different.

Role Summary

We are building a US data science team of three: a lead who owns risk analytics for our customers' track, a data scientist working on the quality of analyst decisions, and you. You are the engineer. What the other two build in notebooks, you turn into systems that run on a schedule, hold up under real data, and can be handed to someone else. That covers the full model lifecycle. Packaging and deployment, the pipelines that feed models, versioning of data and models together, monitoring for drift and degradation, retraining, and the plumbing that gets a result in front of the person who needs it. We are early enough that you get to choose most of this rather than inherit it. You will not be doing this on bare ground. A cloud engineering team across the US and UK looks after our AWS platform, networking, and security, and the UK engineering team runs the data platform and the inspection products. Your work sits on top of theirs, and getting that boundary right is part of the job. We hold years of ultrasonic, induction, and eddy current test data from non-stop inspection across North America. Volume is not the constraint here. Getting reliable, reproducible answers out of it is.

What We Expect From You

We expect an exceptional level of drive and ambition. You think beyond today's work to what the team and organization need next, champion bold ideas, and see them through. Your hunger is infectious - it inspires those around you to aim higher. You should be someone who puts the team first. You share credit openly, admit when you are wrong, and welcome feedback as an opportunity to grow. You are comfortable saying "I don't know" and asking for help when needed. This role requires a high degree of self-direction. You will manage complex work with minimal oversight, identify problems and solutions proactively, and may lead workstreams. You make well-reasoned technical decisions and elevate when there is genuine business or architectural impact. You should be able to quickly grasp complex problems that span multiple systems or domains. We expect you to design effective solutions for non-trivial requirements, identify root causes efficiently, and consider performance, scalability, and maintainability in your approach. You will be the person who insists that a result is reproducible. That is a temperament as much as a skill, and it is the main reason this seat exists as an engineering role rather than a third analyst.

Key Responsibilities

• Take models and analyses from prototype to production, and own them once they are there
• Build and maintain the data pipelines that feed models, working with large-scale rail inspection data including ultrasonic, electromagnetic, and operational sources
• Implement model and data versioning so that any result can be traced back to the code and data that produced it
• Monitor deployed models for drift, degradation, and data quality problems, and build the retraining paths that respond to them
• Build and maintain APIs and services that deliver model output to the people and systems that consume it
• Design and implement the compute and orchestration for training and inference workloads on AWS (S3, Lambda, Glue, Step Functions, SageMaker, or equivalents)
• Set the team's engineering standards: testing, code review, environments, CI/CD, and release practice
• Work with the cloud engineering team on the platform underneath, and with the UK data and platform teams on shared data sources
• Automate the manual steps between an idea and a running model, so the data scientists spend their time on method
• Write clean, tested, well-documented code following engineering best practices
• Participate in code reviews, sprint planning, and technical design discussions
• Document architecture decisions, runbooks, and operational procedures

Required Skills & 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 Qualifications and years of experience are indicative guidelines, not mandatory requirements. These criteria may be met through demonstrated competency or equivalent experience.
• 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.)