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Machine Learning Engineer Opt Jobs in Glendora, NJ

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Machine Learning Engineer Opt information

See Glendora, NJ salary details

$30.3K

$124K

$186.3K

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

As of Aug 6, 2026, the average yearly pay for machine learning engineer opt in Glendora, NJ is $123,977.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,700.00 and $149,200.00 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 into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

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

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

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 a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges machine learning engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
What cities near Glendora, NJ are hiring for Machine Learning Engineer Opt jobs? Cities near Glendora, NJ with the most Machine Learning Engineer Opt job openings:
Machine Learning Engineer Opt jobs near you
Infographic showing various Machine Learning Engineer Opt job openings in Glendora, NJ as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $123,977 per year, or $59.6 per hour.

Principal Data & Machine Learning Engineer

AKUVO LLC

Malvern, PA • On-site

$158K - $216K/yr

Full-time

Posted 14 days ago

Job description

THE OPPORTUNITY

AKUVO is seeking a Principal Data & Machine Learning Engineer to serve as the senior-most technical owner across AKUVO’s data platform, machine-learning models, and the services behind AKUVO IQ. This is a breadth role: you are equally at home building production applications and APIs, engineering the data lake and infrastructure, and developing and deploying predictive models — the person the team turns to at any layer.

You will lead the technical execution of the data and analytics strategy, own architecture across data engineering and machine learning, internalize critical systems currently held by external partners, and provide technical leadership and mentorship to the engineering team. The role combines hands-on engineering across the full stack with technical leadership and direct ownership of production systems.

LOCATION

Local in Malvern/Philadelphia first, widening to surrounding areas such as New Jersey, New York, Delaware, while continuing to expand geographically in a hybrid/remote capacity based on location.

KEY RESPONSIBILITIES

  • Lead the technical execution of the data and analytics strategy across data engineering and machine learning, and own the architecture for AKUVO’s data lake, ML platform, model pipelines, and the data services behind AKUVO IQ.
  • Work hands-on across the full stack — application and API development, systems and infrastructure, data pipelines, and predictive-model development — stepping directly into whichever layer the team needs.
  • Build, deploy, and maintain predictive models and scores alongside the Senior Data & Machine Learning Engineer, contributing directly to model development as well as the platform beneath it.
  • Internalize critical data and ML systems currently held by external partners through a structured knowledge-transfer and documentation process, building internal depth and reducing concentration risk.
  • Design scalable, reliable, and secure architectures for structured portfolio data, predictive-model data, and separately governed PII and AI-conversation data.
  • Own the operational disciplines for pipelines and production models — monitoring, alerting, incident response, versioning, drift detection, and retraining — so systems can be independently deployed, monitored, and enhanced.
  • Evolve technical practices for architecture, development, testing, CI/CD, observability, documentation, and data quality, and ensure data is accurate, timely, and traceable with clear lineage and governance.
  • Provide technical leadership, mentorship, and development to the engineering team, set technical direction, and coordinate delivery.
  • Partner with Applied AI, the Collections domain, Product, Engineering, Architecture & Innovation, and Compliance to keep data, models, and AI systems integrated, governed, and production-ready.
  • Evaluate technical investments, cost, and resource needs; make pragmatic build-versus-buy decisions; and document and prioritize key risks, dependencies, and technical debt.
  • Communicate architecture, risks, and priorities clearly to executive and cross-functional stakeholders, and advance AI-assisted engineering practices across the team.

SKILLS AND EXPERIENCE

  • 10+ years across software/data engineering and machine learning, with hands-on delivery spanning application development, systems and infrastructure, data platforms, and production ML models.
  • 3+ years providing technical leadership and developing engineers.
  • Full-stack breadth — able to build applications and APIs, engineer data pipelines and infrastructure, and develop, deploy, and maintain ML models; the person the team relies on at any layer.
  • Deep, hands-on experience with cloud data and ML platforms in production (Azure strongly preferred) — data lakes, layered architectures, pipelines, product-serving APIs, and model pipelines.
  • Strong Python and SQL, and modern engineering practices (ETL/ELT, CI/CD, observability, testing, environment management).
  • A track record of internalizing critical systems and knowledge through structured transitions, and of setting and evolving technical practices.
  • Ownership of the production model lifecycle — deployment, versioning, monitoring, drift detection, and retraining.
  • Proven ability to translate business and product priorities into scalable roadmaps and pragmatic build-versus-buy decisions.
  • Strong communication with executive, product, and cross-functional stakeholders, and comfort operating as a hands-on technical leader.
  • Active, sophisticated use of AI within your own engineering and leadership workflow.

PREFERRED QUALIFICATIONS

  • Experience spanning both software/platform engineering and applied ML in the same role — a rare full-stack-plus-modeling breadth.
  • Microsoft Fabric and OneLake, or experience leading a Synapse-to-Fabric migration; Databricks or comparable ML platforms.
  • B2B SaaS, fintech, or financial-services background (2+ years), ideally with collections, lending, or credit-scoring exposure.
  • Experience standing up or maturing model governance, documentation, and compliance practices.
  • Experience with sensitive, PII, or regulated data and separately governed data zones.
  • Azure DevOps and structured delivery processes (Epics → Features → Stories → Tasks).