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Entry Level Machine Learning Engineer Jobs in Red Bank, NJ

SeatGeek

Senior Machine Learning Engineer

New York, NY · On-site +1

$145K - $209K/yr

We are looking for Software Engineers with varying levels of experience to join SeatGeek's R&D team ... learning from others Our stack You do not need experience with all of these, but we thought you ...

SeatGeek

Senior Machine Learning Engineer

New York, NY · On-site

$145K - $209K/yr

We are looking for Software Engineers with varying levels of experience to join SeatGeek's R&D team ... learning from others Our stack You do not need experience with all of these, but we thought you ...

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All JobsMachine Learning Engineer JobsEntry Level Machine Learning Engineer JobsEntry Level Machine Learning Engineer Jobs in Red Bank, NJ

Entry Level Machine Learning Engineer information

See Red Bank, NJ salary details

$30.8K

$71.2K

$121.1K

How much do entry level machine learning engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for entry level machine learning engineer in Red Bank, NJ is $71,181.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,800.00 and $80,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Entry Level Machine Learning Engineer position, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are some typical projects or tasks an Entry Level Machine Learning Engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What is an Entry Level Machine Learning Engineer job?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What cities near Red Bank, NJ are hiring for Entry Level Machine Learning Engineer jobs? Cities near Red Bank, NJ with the most Entry Level Machine Learning Engineer job openings:
Entry Level Machine Learning Engineer jobs near you
Infographic showing various Entry Level Machine Learning Engineer job openings in Red Bank, NJ as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $71,181 per year, or $34.2 per hour.

Machine Learning Engineer III - FES

Fanatics Betting & Gaming

New York, NY • On-site

$63 - $84.50/hr

Full-time

Posted 18 days ago

Job description

About Us
Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.
About The Team
We are the Fan Ecosystem Data team, responsible for enhancing decision-making and innovation across the entire Fanatics ecosystem through data and analytics. We build products that turn disparate data streams into real-time actionable insights, empowering teams to unlock greater value for our customers and stakeholders across every Fanatics surface.
We are seeking a Machine Learning Engineer III to own the infrastructure and systems that bring our data science models to life at scale. As our Data Scientists and Data Engineers build the models that understand and predict fan behavior, you build the platforms that serve those models in production.
Responsibilities
  • Own the end-to-end ML infrastructure for recommendation, personalization, and LTV scoring systems, from feature engineering through model deployment and monitoring.
  • Build and maintain real-time and batch feature pipelines that serve low-latency predictions across the FanApp recommendation experience and cross-vertical personalization use cases.
  • Develop and scale model serving infrastructure that supports high-throughput, high-availability prediction across Fanatics' multi-product ecosystem.
  • Partner directly with Data Scientists to productionize LTV, churn, propensity, and ranking models and bridge the gap between experimentation and reliable production systems.
  • Build and maintain embedding pipelines that generate and refresh user and item representations powering personalization and affinity modeling at scale.
  • Implement and maintain A/B testing and experimentation infrastructure that enables reliable measurement of model and feature impact in production.
  • Collaborate with Data Engineers, Analytics Engineers, and Product teams to identify data sources, enforce data quality standards, and ensure models are fed with accurate, timely signals.
  • Drive continuous improvement of model accuracy, latency, and throughput through iterative optimization and monitoring frameworks.

Experience And Skills
  • 3-5+ years in a machine learning engineering or data engineering role, with a degree in a quantitative field (Computer Science, Mathematics, Statistics, Engineering, or equivalent).
  • Strong Python proficiency and deep familiarity with production ML workflows, including packaging, versioning, deployment, and monitoring.
  • Hands-on experience with end-to-end ML platforms such as Databricks, AWS SageMaker, or equivalent, including model registry and serving components.
  • Proven experience building real-time feature pipelines and model serving systems that operate at scale with strict latency and uptime requirements.
  • Experience building or scaling recommendation or ranking systems in production, including embedding pipelines and low-latency inference infrastructure.
  • Solid understanding of distributed systems and large-scale data processing (e.g. Spark, Kafka, or equivalent).
  • Strong SQL proficiency and experience working with relational and dimensional data models.
  • Practical understanding of the mathematics underlying modern ML (linear algebra, probability, optimization) sufficient to partner effectively with Data Scientists on model design and debugging.
  • Familiarity with experimentation infrastructure and A/B testing frameworks, including exposure bias handling and metric integrity in production environments.

Preferred But Not Required
  • Experience with feature stores (e.g. Feast, Tecton) and their role in supporting both real-time and batch ML use cases
  • Experience with ML observability tooling, including drift detection, prediction monitoring, feature freshness alerting

Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE-a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process.
For information about our benefits, please visit https://benefitsatfanatics.com/
Ranges will change based on country and state of residence, which are reflected in Geographical Zones defined by Fanatics Betting and Gaming. The range incorporates all of our Geographical Compensation Zones and is subject to change as the Zone associated with the actual offer is confirmed. In addition to the base and bonus, full-time employment, and more. For information about our benefits, please visit https://benefitsatfanatics.com/
Salary Range
$117,000-$167,000 USD
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