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Senior Machine Learning Engineer Jobs in New Castle, DE

Sr Lead Security Engineer

Wilmington, DE · On-site

$111K - $152K/yr

As a Senior Lead Security Engineer at JPMorganChase within the Cybersecurity and Technology ... machine learning, mobile, etc.) * Extensive experience with threat modeling, discovery ...

Design and implement enterprise-grade Machine Learning platforms capable of deploying and running ... partners, and senior leadership. * Deliver high-quality results within tight deadlines while ...

Sr. Java Developer

Wilmington, DE · On-site

$55.50 - $70.75/hr

... Sr. Java Developer, responsible for executing software solutions, design, development, and ... machine learning, mobile, etc.). • Skills: Java, Spring Boot, AWS. • Exposure to cloud ...

CCB Risk Program Associate

Wilmington, DE · On-site

$57K - $57K/yr

Advanced Machine Learning Techniques: Apply state-of-the-art machine learning methodologies ... Work closely with senior management to develop and implement ambitious, innovative modeling ...

Showing results 41-60

Senior Machine Learning Engineer information

See New Castle, DE salary details

$57.5K

$122.4K

$177.4K

How much do senior machine learning engineer jobs pay per year?

As of Sep 2, 2026, the average yearly pay for senior machine learning engineer in New Castle, DE is $122,362.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,000.00 and $138,700.00 per year, depending on experience, location, and employer.

What does a senior machine learning engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What are the key skills and qualifications needed to thrive as a senior machine learning engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

What is the difference between Senior Machine Learning Engineer vs Data Scientist?

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What cities near New Castle, DE are hiring for Senior Machine Learning Engineer jobs?

Cities near New Castle, DE with the most Senior Machine Learning Engineer job openings:

Infographic showing various Senior Machine Learning Engineer job openings in New Castle, DE as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,724 per year, or $59 per hour.

Vice President - Credit Risk Data Science, Business Banking Risk Modeling

JPMorgan Chase & Co

Wilmington, DE • On-site

Full-time

Medical, Retirement

Posted 9 days ago


JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 499 frontline employees who took The Breakroom Quiz

72nd of 174 rated banks


Job description

Bring your expertise to JPMorgan Chase. As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.
As a Vice President - Credit Risk Data Science, Business Banking Risk Modeling, you will lead advanced feature engineering and machine learning initiatives that power customer analytics and credit risk decisioning across Chase sub-lines of business. You will own the end-to-end design and scaling of an enterprise Risk Attribute Library, ensuring feature quality, lineage, and reusability, while building and improving production-grade models that influence critical business decisions. Starting with a focus on the card business, you will extend solutions across the broader Chase portfolio and drive innovation using modern analytics, deep learning, and large language model enabled approaches.

Job Responsibilities:

  • Lead the design, development, and governance of a scalable enterprise Risk Attribute Library, owning end-to-end attribute and model quality.
  • Drive consistency, reliability, and reuse of features across customer lifecycles and Chase sub-lines of business through rigorous standards and reproducible development practices.
  • Partner with cross-functional teams to ideate, prototype, and productionize advanced feature engineering methods.
  • Engineer high-impact features from large-scale structured and unstructured datasets to improve predictive performance and decisioning outcomes.
  • Build robust machine learning and deep learning models, including Transformer-based approaches, to predict customer behavior and optimize risk strategies.
  • Apply large language model techniques to extract signal from unstructured text (for example, customer interactions, disclosures, narratives) to enhance models and enable new analytics products.
  • Establish attribute quality testing and monitoring frameworks to detect data drift, leakage, instability, and distribution shifts.
  • Implement alerting mechanisms and resolve data or feature issues to maintain accuracy, stability, and consistency in production.
  • Evaluate new internal and external data sources by assessing signal strength, stability, latency, and compliance considerations.
  • Align with risk, marketing, technology, data governance, and controls partners to ensure correct implementation and robust documentation, lineage, and lifecycle management.
  • Communicate complex analytical findings clearly to technical and non-technical stakeholders, translating results into actionable recommendations and measurable business impact.
     

Required Qualifications, Capabilities, and Skills:

  • Master's degree or Doctor of Philosophy degree in Computer Science, Mathematics, Statistics, Econometrics, Engineering, or a related quantitative discipline.
  • 5+ years of experience working with large-scale data and developing, managing, or implementing attributes and predictive models.
  • 5+ years of professional coding experience with demonstrated ability to write high-quality, production-ready code.
  • Proficiency in one or more of the following: Python, Statistical Analysis System, Apache Spark, Scala, or equivalent data and machine learning programming stacks.
  • Experience with modern machine learning and deep learning frameworks and platforms such as TensorFlow (or equivalent), Amazon Web Services cloud, Snowflake, and or Databricks.
  • Strong understanding of statistical and machine learning methods such as generalized linear models and regression, decision trees, random forests, boosting, clustering, k-nearest neighbors, anomaly detection, simulation, scenario analysis, and modeling.
  • Demonstrated ability to perform feature engineering, model validation, and performance evaluation in a regulated or controlled environment.
     

Preferred Qualifications, Capabilities, and Skills:

  • consumer lending experience strongly preferred

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs. 

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.  We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. 

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

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