Machine Learning Jobs in Jersey City, NJ

Lead design and deliver reusable, cloud-native components and SDKs; write and review secure production code; automate remediation and CI/CD; drive adoption through communities of practice; evaluate vendor and internal architectures, and improve operational stability across the enterprise.

Ten-week paid AI internship applying artificial intelligence, machine learning, data analysis, automation, and emerging technologies to enterprise business challenges. Interns collaborate across technology, product, analytics, HR, and finance; build applied AI solutions, dashboards, and process improvements; participate in professional development and networking; and present results to leadership. The role is hybrid in Jersey City, New Jersey, requiring at least three office days weekly.

Design, build, and deploy scalable AI and ML solutions by wrangling and integrating data, implementing models (Python/Java, TensorFlow, Scikit-Learn), developing data pipelines and NLP applications, collaborating with clients, and mentoring junior team members to drive business insights and regulatory risk solutions.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

Provide commercial legal support for enterprise cloud, SaaS, and infrastructure transactions. Draft, negotiate, and advise on contracts (SaaS, IaaS, software licensing, DPA), counsel sales and cross-functional teams on legal/privacy/commercial risk, manage concurrent deals, improve templates and processes, and coordinate outside counsel to enable scalable, business-focused legal support for growth.

Lead design, develop, and deploy secure, scalable full-stack applications (Java or Python) for finance planning and analysis. Drive architecture reviews, operational stability, CI/CD automation, cloud-native AWS deployments, and integration of AI/ML/LLM capabilities. Mentor teams and lead engineering communities of practice.

Lead and scale MEDHOST’s software engineering organization, overseeing product delivery, modernization, security, reliability, scalability, and operational performance. Develop technology strategy and architecture roadmaps while driving AI-first transformation across products, engineering workflows, and enterprise operations. Establish performance metrics, govern technology investments, manage strategic initiatives, and advise executives on emerging technologies, risks, and opportunities. The role also requires implementing generative AI, machine learning, and AI governance within healthcare software environments.

Own the strategy, roadmap, and delivery of AI-powered Capital Management and Lending tools. Lead engineering teams, prototype AI solutions, translate banking requirements into technical designs, and collaborate with senior stakeholders. Applications include capital allocation, RWA optimization, credit underwriting, predictive modeling, document intelligence, covenant monitoring, and stress testing. Ensure solutions meet security, ethical, regulatory, Fair Lending, Basel III/IV, CCAR, and model risk requirements.

Translates business requirements into scalable AI and GenAI solutions, supporting implementation, model deployment, testing, systems integration, customization, scripting, and troubleshooting. Partners with business users, data scientists, and ML engineers; gathers product feedback; trains teams on prompt engineering and AI operations; and assesses risks involving privacy, bias, ethics, and model limitations. Requires autonomous stakeholder advising and mentoring of junior analysts.

Lead design and delivery of secure, scalable AI/ML and generative/agentic solutions: develop LLMs and agent frameworks, productionize end-to-end models, mentor teams, collaborate with stakeholders, and drive R&D and PoCs to production.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Lead supply chain analytics consulting engagements, offshore teams, and client discussions. Translate supply chain business challenges into scalable data and analytics solutions using Python, SQL, machine learning, AI, optimization, and operations research. Provide domain expertise across demand and supply planning, shipping, and inventory management; mentor analysts and data professionals; architect analytics frameworks; and communicate actionable insights to business stakeholders.

Leads and builds the pharmaceutical Business Intelligence and Analytics function, partnering with commercial leadership and cross-functional teams. Owns data strategy, integration, reporting, dashboards, forecasting models, market research, patient-finding analytics, omnichannel measurement, field-force reporting, and business development assessments. Uses SQL Server, Excel, Tableau or Power BI, advanced analytics, AI, and machine learning to generate actionable insights, improve patient access, and support commercial decisions.

Build and operate scalable machine learning training platforms and pipelines across AWS and other cloud environments. Optimize GPU-based single-node and distributed training for performance, cost, stability, and reproducibility. Develop Kubernetes infrastructure, GenAI and LLM fine-tuning workflows, observability, CI/CD automation, governance controls, and developer self-service tools. Partner with data engineering and platform teams while applying secure, responsible AI-assisted development practices.

Lead software engineers and applied AI/ML practitioners while developing secure, scalable production systems in Java or Python. Integrate LLMs, generative AI, NLP, RAG, and coding assistants into software engineering workflows. Establish responsible AI governance, validation standards, testing, security, and operational practices. Guide system architecture, experiments, algorithm implementation, productionization, code quality, automation adoption, and technical decision-making across global teams.

Drive AI/ML solution sales for cable, media, and entertainment clients by developing POCs, proposing solutions, advising customers, building roadmaps, coordinating offshore delivery teams, and performing data-driven analysis to identify business improvements and implementation plans.

Data Science interns apply analytical and data science methods to business and engineering problems. Responsibilities include data profiling, feature engineering, identifying patterns and anomalies, building analytical models and automated tools, and partnering with engineering and business teams to deliver actionable recommendations. Interns gain experience with programming, advanced analytics, machine learning, distributed computing, and scalable data-driven solutions through a structured 10-week program.

Entry-level role supporting data, analytics, technology and transformation projects. Participate in classroom training, complete assigned projects, follow coding and documentation best practices, build data-driven presentations, and analyze data to support decision-making while collaborating across Data-Analytics, Technology, and Transformation teams.

Leads development of secure, scalable Java and AWS software systems, including microservices, event-driven platforms, and production machine-learning systems. Designs data and feature pipelines, model deployment and monitoring, automation, CI/CD, and operational stability practices. Guides code reviews, testing, secure coding, vendor evaluations, and responsible adoption of AI-assisted engineering tools. Collaborates across product, data, platform, SRE, and governance teams while promoting MLOps, LLM, observability, and model-monitoring practices.

The Senior Product Manager will define the product strategy and roadmap for AI and data products, lead product initiatives, and collaborate with engineering and data science teams. The role requires transforming complex problems into actionable products that enhance healthcare decision-making.

Define and drive multi-year fraud AI/ML platform strategy and roadmap across models, feature infrastructure, graph intelligence, and MLOps. Manage experimentation, productize graph capabilities, partner cross-functionally, and deliver measurable reductions in fraud loss while preserving customer experience.

The Solutions Engineer designs and delivers technical solutions for customer deployments, integrations, and implementations. Responsibilities include gathering requirements, integrating APIs and SFTP solutions, conducting demos and proofs of concept, creating technical documentation, handling security questionnaires, and ensuring projects meet scope, budget, and timelines. The role partners with sales, product, solution architecture, and regional teams while maintaining customer satisfaction and identifying innovative use cases.

Lead architecture and delivery of production AI/ML solutions for wholesale payments, building NLP/document-AI and LLM-enabled applications on AWS, establishing MLOps and model governance, measuring business outcomes, and mentoring engineers.

Lead engineer responsible for deploying and serving end-to-end ML models and cloud-native services, driving AI-assisted engineering practices, producing secure, high-quality production code, influencing product design and operations, and mentoring technical teams.

Lead design and delivery of reliability, observability, and SRE practices for large-scale data and AI/ML platforms. Define NFRs, SLI/SLOs, incident response, and implement reliable, secure, scalable infrastructure and automation while mentoring teams and driving safe, auditable AI-assisted operations.