Machine Learning Jobs in Jersey City, NJ

Sell Infoblox cloud-first networking and security solutions in the Greater Philadelphia area. Own full sales cycle: prospecting, new logo acquisition, account expansion, forecasting, and pipeline management. Collaborate with BDRs, solutions architects, and marketing; articulate AI/ML solution value to executive and technical stakeholders and leverage AI tools to drive sales activities and close deals.

Build and maintain high-performance web scraping pipelines: develop extraction code, handle dynamic content and pagination, ensure data quality and storage, monitor and scale distributed scraping infrastructure, and optimize processes for large-scale AI data ingestion.

Lead polyglot engineering to design, build, and scale end-to-end systems across backend, frontend, data, and infrastructure. Prototype and productionize multi-language solutions, architect microservices and distributed systems, promote CI/CD, security, and performance best practices, and mentor engineers to drive platform reuse and enterprise agility.

The role leads application systems analysis and programming, drives architecture strategy, evaluates technologies, and influences senior stakeholders in the investment domain.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Design, build, and operate production-grade GenAI and RAG platforms with guardrails, observability, evaluation frameworks, CI/CD, cost/performance monitoring, safety mechanisms, and incident management. Partner with product and governance teams, mentor engineers, and ensure enterprise reliability, security, and compliance across the AI lifecycle.

As a Senior Machine Learning Engineer, you'll design data pipelines, develop prediction systems, and optimize model deployment processes while collaborating with cross-functional teams.

Design, build, and operate high-performance model serving platforms for LLMs, vision, and recommendation models. Optimize inference (batching, caching, autoscaling), tune GPU utilization, implement routing and QoS, integrate observability, run incident response, and support deployment workflows and security controls for production AI services.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

Build production-grade AI systems for alternative investments: parse complex financial documents, create embedding pipelines, implement semantic search and vector retrieval, develop RAG solutions with LLMs, integrate AI workflows into enterprise data pipelines, expose capabilities via APIs, provide architecture recommendations, and mentor junior engineers.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

The Principal ML Ops Engineer will design, implement, and scale ML infrastructure, focusing on optimizing AI systems, collaborating with teams, and establishing best practices for ML Ops.

Lead AI and data transformation initiatives, develop AI strategies, deliver AI/ML solutions, mentor teams, and drive innovation for enterprise clients.

Design, develop, and deploy AI-driven applications using Generative AI and LLMs. Manage projects from conception to deployment and ensure compliance with regulations.

Build and maintain CI/CD and IaC pipelines for ML infrastructure across AWS, GCP, and Azure. Enable secure, automated deployments and monitoring for LLMs and agentic flows. Produce production-ready code, documentation, and support production incidents via on-call rotation. Stay current with AI/ML advancements and collaborate with business customers to deliver scalable platform solutions.

Lead the design and optimization of batch and streaming data pipelines, ensuring high-quality data delivery for AI and analytics, while mentoring and promoting engineering best practices.