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Ml Inference Jobs in Camden, NJ (NOW HIRING)

URBN

URBN Staff Engineer, GenAI

Philadelphia, PA · On-site

Implement and maintain ML architecture, including pipelines and applications that enable training and inference of generative models in production. Champion best practices in full-stack algorithm ...

URBN

URBN Staff Engineer, GenAI

Philadelphia, PA · On-site +1

Implement and maintain ML architecture, including pipelines and applications that enable training and inference of generative models in production. Champion best practices in full-stack algorithm ...

UHS

Software Engineer II - AI Solutions

King Of Prussia, PA

$95K - $130K/yr

Develop and maintain AI/ML Solutions: Design and implement AI-driven capabilities that solve real operational problems in healthcare. This includes building inference pipelines, integrating models ...

Anaplan

Principal Engineer, AI

Philadelphia, PA · On-site

... deep ML knowledge and strong software engineering skills. Your Impact * Lead the architecture ... Optimize model inference pipelines for performance, cost, and scalability in production ...

URBN

URBN Staff Software Engineer

Philadelphia, PA · On-site

Collaborate with the team to implement and maintain the ML architecture, including data pipelines and applications that enable training and inference of ML models in production. * Foster strong cross ...

URBN

URBN Staff Software Engineer

Philadelphia, PA · On-site

Collaborate with the team to implement and maintain the ML architecture, including data pipelines and applications that enable training and inference of ML models in production. * Foster strong cross ...

Maxonic Inc.

Senior Principal AI Engineer

King Of Prussia, PA · On-site

$122K - $169K/yr

This role requires both traditional AI/ML, GenAI product engineering, and traditional software ... Optimize the performance, cost (token and inference economics), scalability, and reliability of AI ...

UHS

Software Engineer II - AI Solutions

King Of Prussia, PA · On-site

$95K - $130K/yr

Develop and maintain AI/ML Solutions: Design and implement AI-driven capabilities that solve real operational problems in healthcare. This includes building inference pipelines, integrating models ...

Gnostech

Software Engineer (US-Remote)

Marlton, NJ · On-site +1

Integrate AI models, data pipelines, and inference services into production systems. * Collaborate ... Experience with AI/ML technologies, including machine learning frameworks (TensorFlow, PyTorch) or ...

Vangard, Inc.

Senior Machine Learning Engineer

Malvern, PA · On-site

$102K - $140K/yr

Engineer scalable training, inference, and retraining workflows using AWS SageMaker. * Develop and ... Establish engineering standards, testing frameworks, and governance controls for ML solutions.

Slalom Consulting

General Information

Philadelphia, PA · On-site

$60.50 - $78.75/hr

Description and Requirements AI/ML Ops Engineer Location: Remote / Hybrid (Client-Facing Consulting ... Implement scalable model-serving architectures, including real-time APIs, batch inference pipelines ...

Jj

Director HTA Statistics & RWE Methods

Horsham, PA

$164 - $282.90/hr

... inference methods, surrogate endpoint validations, etc.) to support market access and launch ... AI/ML advanced analytics scaling and platforms, etc. Qualifications Education * Required:

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All JobsMl Inference JobsMl Inference Jobs in Camden, NJ

Ml Inference information

See Camden, NJ salary details

$37.8K

$123.8K

$198.2K

How much do ml inference jobs pay per year?

As of Sep 8, 2026, the average yearly pay for ml inference in Camden, NJ is $123,829.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,400.00 and $137,200.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What job categories do people searching Ml Inference jobs in Camden, NJ look for?

The top searched job categories for Ml Inference jobs in Camden, NJ are:

What cities near Camden, NJ are hiring for Ml Inference jobs?

Cities near Camden, NJ with the most Ml Inference job openings:

Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Camden, NJ as of June 2026, with employment types broken down into 85% Full Time, 7% Part Time, and 8% Contract. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution, with an average salary of $123,829 per year, or $59.5 per hour.

URBN Staff Engineer, GenAI

URBN

Philadelphia, PA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 26 days ago

URBN rating

6.7

Company rating: 6.7 out of 10

Based on 54 frontline employees who took The Breakroom Quiz

33rd of 105 rated fashion retailers

Job description

Role Summary
URBN is hiring a Staff Engineer (GenAI) to join the development of AI-powered visual experiences, with a primary focus on building and operationalizing image and video generation systems. We are looking for an experienced engineer to join our mission of integrating generative AI solutions with creative tools and production workflows. In this role, you will own the engineering side of multi-model generative pipelines, turning research prototypes into reliable, scalable services that power AI-first innovations across our digital ecosystem. You will collaborate with a talented cross-functional team comprising data scientists, UX designers, product managers, creative partners, and domain experts to deliver significant business impact.
This role will focus on making generative systems robust, efficient, and production-ready. The ideal candidate brings deep software engineering fundamentals, comfort with agentic AI tooling, and enough generative AI fluency to wrangle prompts, orchestrate multi-step visual workflows, and reason about output quality, even if they are not the one doing final model tuning or evaluation design.
If you are energized by the intersection of software engineering and generative AI, and you want to build the infrastructure and tooling that turns cutting-edge image and video models into real products, we invite you to help shape the future of intelligent, AI-native creative experiences at URBN.
Role Responsibilities
  • Design, build, and optimize image and video generation pipelines (generation, inpainting, upscaling, style transfer, conditioning, post-processing) into production-ready, observable services with attention to cost, latency, and throughput.
  • Design and develop agentic workflows (ADK, A2A, LangGraph, or similar), MCP servers (FastMCP or similar), and microservices (FastAPI, GraphQL, or similar) to orchestrate and serve generative AI capabilities at scale.
  • Develop prompt management systems and structured prompting strategies for consistent visual output, learning on the job how to wrangle prompts for fashion-specific and virtual try-on use cases. Engineer consistency mechanisms and quality gates in partnership with data scientists who own evaluation methodology.
  • Integrate multimodal and vision-language models into production workflows for image understanding, automated tagging, captioning, and quality pre-screening.
  • Collaborate with Product Designers, Product Managers, Data Scientists, and other Engineers to translate brand and business needs into scalable generative AI solutions. Evaluate new technologies, models, and vendors through proof-of-concept studies.
  • Implement and maintain ML architecture, including pipelines and applications that enable training and inference of generative models in production. Champion best practices in full-stack algorithm engineering.

Role Qualifications
  • Generative AI Systems: 1+ year of hands-on experience building or operationalizing image generation systems (diffusion models, multimodal pipelines) in a professional applied context. Working familiarity with the rapidly evolving landscape of image and video generation models and orchestration patterns.
  • AI-Augmented Development & Agentic AI: Proficient with AI-powered development tools (e.g., Cursor, VS Code Copilot, Claude Code, or Gemini). Experience designing agentic workflows or AI orchestration systems using LangGraph, ADK, A2A, CrewAI, or similar. Familiarity with MCP (Model Context Protocol) is a strong plus.
  • Python & ML Frameworks: Strong proficiency in Python, with practical experience using PyTorch and/or Hugging Face for model serving, fine-tuning support, and inference optimization.
  • APIs, Distributed Systems & Cloud: Strong background in scalable RESTful APIs, microservices architecture, and high-availability distributed systems. Proficiency with Docker, container orchestration, cloud-native services, and cloud-based AI infrastructure. Experience with Terraform or similar IaC tooling.
  • Engineering Practices: Dedication to CI/CD pipelines, TDD, and automated code quality standards. Excellent communicator who can bridge technical, data science, and creative teams. Comfortable with ambiguity and minimal oversight.
  • Education: Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience.
Nice to Haves
  • Experience with video generation models, or with fashion/retail/e-commerce applications of generative AI such as virtual try-on, AI product photography, or on-model image generation.
  • Experience building tooling and orchestration for model fine-tuning workflows (LoRA, DreamBooth, ControlNet, IP-Adapter, textual inversion), enabling data scientists to iterate quickly on adaptation techniques.
  • Background in computer vision fundamentals (segmentation, detection, embeddings) or LLM evaluation frameworks for generative outputs.
  • Experience with prompt engineering at scale, creative design tools (Adobe Photoshop, Firefly, Figma), or data pipeline orchestration (Airflow, dbt, or similar).

The Perks
URBN offers comprehensive Perks & Benefits to employees. Availability and eligibility to specific benefits may be subject to your location and employment status. Benefits include medical, dental, vision, PTO, generous employee discounts, retirement savings and much more! For additional information visit www.urbn.com/work-with-us/benefits
EEO Statement
URBN celebrates diversity and is committed to creating an inclusive environment for all employees. We are proud to provide equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, sex (including gender, pregnancy, sexual orientation, and gender identity or expression), religion, creed, age, physical or mental disability, national origin or ancestry, ethnicity, citizenship, service in the uniformed services, genetic information, or any other protected characteristic as established by law. We believe strongly in fostering a safe, fair and respectful work environment. To ensure compliance with our non-discrimination and anti-harassment policies, we offer anti-harassment training to managers and employees.

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