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Machine Learning Data Associate Jobs in Wallingford, CT

Sperry Rail, Inc.

Machine Learning Engineer

Shelton, CT · On-site

Machine Learning Engineer Sperry Rail, Inc. Shelton, Connecticut, United States About this position ... Role Summary We are building a US data science team of three: a lead who owns risk analytics for ...

The Hartford

Sr AI Machine Learning Engineer

Hartford, CT · Hybrid

$117K - $175K/yr

Sr Data Engineer - GE07BE We're determined to make a difference and are proud to be an insurance ... The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations ...

The Hartford

Sr AI Machine Learning Engineer

Hartford, CT · Hybrid

$117K - $175K/yr

Sr Data Engineer - GE07BE We're determined to make a difference and are proud to be an insurance ... The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations ...

SynergisticIT

Backend Developer - Remote

New Haven, CT · On-site

$50.75 - $65.75/hr

For data science/machine learning positions required skills include a bachelors degree or masters degree in computer science, computer engineering, electrical engineering, information systems, IT ...

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Machine Learning Data Associate information

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How much do machine learning data associate jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for machine learning data associate in Wallingford, CT is $18.79, according to ZipRecruiter salary data. Most workers in this role earn between $15.43 and $20.00 per hour, depending on experience, location, and employer.

What is a machine learning data associate?

Machine Learning Data Associates are professionals who support the development of machine learning models by preparing, labeling, and validating data sets. Their work ensures that data used for training algorithms is accurate, consistent, and properly annotated. They may also assist with data cleaning, quality checks, and sometimes basic data analysis tasks. This role is crucial in industries where high-quality labeled data is essential for building effective AI systems.

What are the key skills and qualifications needed to thrive as a machine learning data associate?

To thrive as a Machine Learning Data Associate, you need strong analytical skills, attention to detail, and a basic understanding of data annotation and labeling processes, often supported by a degree in computer science or a related field. Familiarity with data management tools, annotation platforms, and sometimes scripting languages like Python is typically required. Strong communication, collaboration, and problem-solving abilities help you work efficiently with data science teams and ensure high-quality outcomes. These skills and qualities are crucial for producing accurate datasets that directly impact the effectiveness of machine learning models.

How does a machine learning data associate typically collaborate with data scientists and engineers within a project team?

As a Machine Learning Data Associate, you play a vital role in supporting data scientists and engineers by annotating, cleaning, and organizing large datasets to ensure high data quality. You'll frequently communicate with team members to clarify labeling guidelines, provide feedback on data inconsistencies, and report any edge cases encountered during annotation. This collaboration ensures that the datasets used for training machine learning models are accurate and comprehensive, directly impacting the success of the project. Expect regular team meetings and ongoing feedback loops to maintain alignment with evolving project requirements.

What is the difference between Machine Learning Data Associate vs Data Analyst?

AspectMachine Learning Data AssociateData Analyst
Required SkillsData cleaning, labeling, basic programming, understanding of ML workflowsData interpretation, visualization, statistical analysis
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing, healthcare sectors
Common CertificationsData Science certifications, Python, SQLExcel, Tableau, SQL certifications

The main difference is that Machine Learning Data Associates focus on preparing and labeling data specifically for machine learning models, while Data Analysts interpret data to generate insights for business decisions. Both roles require strong data skills and often overlap, but their primary objectives and work environments differ.

How do I become a machine learning data associate?

To become a machine learning data associate, candidates typically need a high school diploma or equivalent, with some roles preferring a bachelor's degree in computer science, data science, or related fields. Relevant skills include data annotation, understanding of machine learning concepts, and proficiency with tools like Excel, SQL, or data labeling platforms. Gaining experience through internships or certifications can improve job prospects in this field.

Is a Machine Learning Data Associate a good job?

A Machine Learning Data Associate role involves preparing and managing data for machine learning models, often requiring skills in data cleaning, annotation, and familiarity with tools like Python or SQL. It can be a good entry-level position for those interested in AI and data science, offering opportunities to develop technical skills and gain industry experience. Compensation and job satisfaction vary depending on the employer and location, but it generally provides a solid foundation for a career in machine learning or data analysis.

What cities near Wallingford, CT are hiring for Machine Learning Data Associate jobs?

Cities near Wallingford, CT with the most Machine Learning Data Associate job openings:

Machine Learning Data Associate jobs near you
Infographic showing various Machine Learning Data Associate job openings in Wallingford, CT as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $39,080 per year, or $18.8 per hour.

Machine Learning Engineer

Shelton, CT • On-site

Sperry Rail, Inc.
Trucking • 501 - 1,000 employees
Learn more about Sperry Rail, Inc.external

Other

Posted 23 days ago

Sperry Rail Service rating

6.9

Company rating: 6.9 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

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.)
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