Machine Learning Engineer
Shelton, CT · On-site
Our global engineering teams work collaboratively to develop step-change technologies that define ... machine learning models or statistical analyses into production and keeping them running • ...
Machine Learning Engineer
Shelton, CT · On-site
Our global engineering teams work collaboratively to develop step-change technologies that define ... machine learning models or statistical analyses into production and keeping them running • ...
Machine Learning Engineer
Shelton, CT · On-site
$140 - $190/hr
You are the engineer. What the other two build in notebooks, you turn into systems that run on a ... Experience putting machine learning models or statistical analyses into production and keeping them ...
Machine Learning Engineer
Shelton, CT · On-site
$140 - $190/hr
You are the engineer. What the other two build in notebooks, you turn into systems that run on a ... Experience putting machine learning models or statistical analyses into production and keeping them ...
Machine Learning Engineer
Shelton, CT · On-site
$120 - $180/hr
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 ...
Machine Learning Engineer
Shelton, CT · On-site
$120 - $180/hr
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 ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
Machine Learning Engineer
Hamden, CT · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
Machine Learning Engineer
Hamden, CT · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
Machine Learning Engineer
Bridgeport, CT · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
Machine Learning Engineer
Bridgeport, CT · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...
Machine Learning Engineer information
See West Haven, CT salary details
$30.6K - $44.9K
1% of jobs
$44.9K - $59.2K
1% of jobs
$59.2K - $73.5K
5% of jobs
$73.5K - $87.8K
6% of jobs
$99.6K is the 25th percentile. Wages below this are outliers.
$87.8K - $102.1K
14% of jobs
$102.1K - $116.4K
14% of jobs
The median wage is $123.5K / yr.
$116.4K - $130.7K
18% of jobs
$130.7K - $145K
14% of jobs
$147.9K is the 75th percentile. Wages above this are outliers.
$145K - $159.3K
12% of jobs
$159.3K - $173.6K
11% of jobs
$173.6K - $187.9K
5% of jobs
$30.6K
$125K
$187.9K
How much do machine learning engineer jobs pay per year?
As of Sep 4, 2026, the average yearly pay for machine learning engineer in West Haven, CT is $125,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $150,500.00 per year, depending on experience, location, and employer.
What is a machine learning engineer?
Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.
What does a machine learning engineer do?
A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.
What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?
To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.
What are some common challenges faced by machine learning engineers when deploying models to production?
Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.
What is the difference between Machine Learning Engineer vs Data Scientist?
| Aspect | Machine Learning Engineer | Data Scientist |
|---|---|---|
| Credentials | Bachelor's or Master's in CS, Data Science, or related; experience with ML frameworks | Bachelor's or Master's in Statistics, Data Science, or related; strong analytical skills |
| Work Environment | Develops scalable ML models, deploys algorithms into production | Analyzes data, builds models, interprets data insights |
| Industry Usage | Tech companies, startups, AI-focused firms | Finance, healthcare, marketing, research organizations |
While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.
More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in West Haven, CT?
The most popular types of Machine Learning Engineer jobs in West Haven, CT are:
What are popular job titles related to Machine Learning Engineer jobs in West Haven, CT?
For Machine Learning Engineer jobs in West Haven, CT, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in West Haven, CT look for?
The top searched job categories for Machine Learning Engineer jobs in West Haven, CT are:
What cities near West Haven, CT are hiring for Machine Learning Engineer jobs?
Cities near West Haven, CT with the most Machine Learning Engineer job openings:
Sperry Rail Service rating
6.9
Based on 8 frontline employees who took The Breakroom Quiz
372nd of 452 rated engineering
Job description
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 escalate 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.
Desirable Skills
• 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.)
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 escalate 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.
Desirable Skills
• 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.)
What Sperry Rail Service employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Sperry Rail
Sourced by ZipRecruiter
Industry
Trucking
Company size
501 - 1,000 Employees
Headquarters location
Shelton, CT, US
Year founded
1928