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Ml Inference Jobs in Camden, NJ (NOW HIRING)

Globo Language Solutions

AI/ML Engineer

Philadelphia, PA · On-site

$109K - $131K/yr

Optimize model selection, prompt size, token usage, batching, caching, inference frequency, and ... or ML engineering. * Experience with the below tech stack is required: * Python (advanced ...

Deloitte

AI Engineer Consultant

Philadelphia, PA · Hybrid

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Damco Solutions

GenAI Architect

Mount Laurel, NJ · On-site

Responsibilities : • Build scalable AI/ML systems; including data pipelines; model training workflows; and inference services. • Evaluate and integrate open‐source and commercial LLMs (e.g.

Five Below

Solutions Architect - AI

Philadelphia, PA · On-site

$60.25 - $79.25/hr

... ML systems in production. * Define and enforce secure-by-design standards for model development, training data handling, inference APIs, and GenAI integrations. * Architect defenses against AI ...

Five Below, In.c

Solutions Architect - AI

Philadelphia, PA · On-site

$63.50 - $83.75/hr

... ML systems in production. * Define and enforce secure-by-design standards for model development, training data handling, inference APIs, and GenAI integrations. * Architect defenses against AI ...

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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 Aug 12, 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 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 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 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.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.
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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.

AI/ML Engineer

Globo Language Solutions

Philadelphia, PA • On-site

$109K - $131K/yr

Full-time

Posted 4 days ago

Job description

Description:


About the Role:

Reporting to the Director of Data & AI Engineering, the AI/ML Engineer is a mid-level, hands-on technical role responsible for building and maintaining the data pipelines, AI models, and intelligent features that power the GLOBO platform. This role spans the full lifecycle of AI development—from cleaning and preparing data, to building and evaluating models, to shipping production features that directly improve operational efficiency and customer experience.


The AI/ML Engineer works across GLOBO’s modern data stack (Fivetran, dbt, Snowflake) and AI infrastructure (AWS Bedrock, LLMs, agentic frameworks) to deliver reliable, well-tested solutions. This person is equally comfortable wrangling messy data and prompt-engineering an LLM, and takes pride in writing clean, tested code that other engineers can build on.


Data Engineering & Pipeline Development:

  • Build and maintain reliable ingestion pipelines usingSnowflake Openflow, Python, and Snowflake, including API, PostgreSQL, and CDC-based integrations.
  • Develop incremental synchronization, cursor/state management, retry logic, schema-drift handling, soft-delete propagation, and source-to-target reconciliation.
  • Transform raw source data through staging, intermediate, and core models into trusted datasets for analytics, reporting, and machine-learning workloads.
  • Apply data-quality checks for freshness, completeness, uniqueness, referential integrity, valid relationships, and business-rule compliance.
  • Maintain source definitions, model documentation, lineage, metadata, and data contracts.
  • Collaborate with data owners to ensure PII/PHI classification, masking, retention, and deletion requirements are implemented throughout the pipeline.

Model Development, Testing & Evaluation:

  • Implement monitoring and alerting for ingestion failures, pipeline freshness, schema changes, data-quality failures, transformation errors, model drift, and inference degradation.
  • Establish automated regression testing for dbt models, features, evaluation datasets, prompts, and model outputs.
  • Validate that sensitive data is appropriately masked, redacted, access-controlled, and excluded from unauthorized model training or data-sharing workflows.
  • Build safeguards for PII/PHI in recorded-call, transcript, and AI/ML processing pipelines, including verification that redaction and deletion workflows complete successfully.
  • Ensure AI/ML outputs are traceable to their source data, model or prompt version, feature set, and evaluation results.
  • Define recovery procedures, data-quality escalation paths, and operational runbooks for critical pipelines and models.
  • Support human review and approval for model outputs that may affect customers, interpreters, employees, financial activity, or service quality.

Feature Development & Integration:

  • Collaborate with Product and Engineering to ship AI-powered features into the GLOBO platform.
  • Build and deploy LLM integrations (AWS Bedrock, Anthropic Claude) and agentic workflows (CrewAI, LangChain).
  • Write production-quality code with proper tests, documentation, and error handling.

Reliability & Safety:

  • Implement guardrails, monitoring, and alerting for AI services in production.
  • Ensure AI outputs are consistent and trustworthy.
  • Contribute to evaluation datasets, prompt versioning, and regression testing for deployed models.

Performance & Cost Optimization:

  • Monitor and optimizeSnowflake compute, storage, query performance, dbt execution, Openflow runtime usage, and model-inference costs.
  • Design efficient incremental models, CDC pipelines, materializations, clustering strategies, and warehouse/task schedules.
  • Compare and optimize ingestion costs as Globo transitions from Fivetran to Snowflake Openflow.
  • Reduce unnecessary full refreshes, duplicate processing, excessive data movement, and inefficient feature recomputation.
  • Optimize model selection, prompt size, token usage, batching, caching, inference frequency, and routing between model providers.
  • Measure model performance against operational cost, latency, throughput, and data-freshness requirements.
  • Establish practical service-level targets for critical datasets, transformations, batch jobs, and model-serving workflows.


Requirements:

Required Minimum Education and Experience:

  • Bachelor’s Degree in Computer Science, Data Science, Information Systems, or related field.
  • 2+ years of experience in data engineering, software development, or ML engineering.
  • Experience with the below tech stack is required:
    • Python (advanced proficiency)
    • SQL (advanced proficiency)
    • LLM Integration (AWS Bedrock, Anthropic Claude, or OpenAI API)
    • dbt (data transformation and testing)
    • Snowflake (or similar cloud data warehouse)
    • AWS Lambda / Serverless architecture
  • Experience with the below tech stack is preferred:
    • Fivetran (or similar ELT/ingestion tooling)
    • Agentic Frameworks (CrewAI, LangChain, or similar)
    • Airflow (or similar workflow orchestration)
    • Vector Databases (Pinecone, PGVector, or OpenSearch)
    • AWS ECS/EKS
    • CDK and CloudFormation for automated deployments
    • Ruby on Rails (ability to read/debug core platform code)
    • Redis
    • PostgreSQL
    • React
  • Familiarity with model evaluation techniques, prompt engineering, and AI safety best practices.
  • Experience with Google Docs and Apple/Mac Operating System preferred


Additional Preferred Requirements:

  • Ability to work independently in a decentralized environment without the reliance on direct authority
  • Highest level of personal and professional integrity and ethics
  • Broad understanding of current and emerging technology practices
  • High level of initiative, accountability, and follow-through
  • Value strong teamwork and collaboration skills
  • Demonstrated problem-solving and decision-making skills
  • Ability to manage multiple initiatives and projects and prioritize needs
  • Strong sense of service and passion for the company and business
  • Authorized to legally work for any employer in the United States
  • Willingness to submit to any requested background checks
  • Fluent in English

About GLOBO:GLOBO is a B2B communication platform provider, specializing in translation and interpretation technology, services, data, and insights. For the third year in a row, GLOBO has been ranked in the top-10 on the Philadelphia 100 list of fastest-growing privately held companies.What’s it like to work here? We’re a close-knit team with big ideas and ambitions. We make the impossible happen, and make hard tasks easier. We don’t take ourselves too seriously, but we’re serious about our mission—helping people communicate when it matters most.