Machine Learning Jobs in Jersey City, NJ

Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.

Owns P&L performance, profitability, growth, and digital transformation for Small and Lower Mid-Market commercial insurance portfolios. Leads pricing, underwriting, portfolio monitoring, analytics, data visualization, predictive modeling, and machine learning initiatives. Partners with actuarial, product, engineering, technology, and distribution teams to implement process improvements and underwriting actions across digital channels and geographies. Communicates insights to stakeholders and senior leadership while leading cross-functional teams in an Agile operating model.

Leads the vision, roadmap, delivery, governance, and adoption of Data and AI products supporting P&C insurance underwriting. Partners with underwriting, actuarial, claims, distribution, engineering, data science, and executive stakeholders to deliver capabilities such as GenAI copilots, intelligent risk scoring, submission automation, predictive models, and analytics. Owns prioritization, agile delivery, responsible AI practices, stakeholder reviews, and business value measurement.

Lead data science and marketing analytics initiatives that improve member activation, engagement, retention, and health outcomes. Develop segmentation, propensity, predictive, and prescriptive models; design experiments and A/B tests; establish measurement frameworks; and translate insights into campaign and product strategies. Partner with Product, Marketing, Care Management, Data Engineering, and MLOps teams to operationalize targeting models and scale personalized engagement programs.

Performs advanced risk analytics, reporting, data management, visualization, and modeling to identify portfolio trends, risks, control gaps, and business opportunities. Develops SQL-based datasets, dashboards, and executive reporting; investigates data quality and control issues; supports regulatory requirements; and partners with senior stakeholders on strategic initiatives. Uses automation and approved AI tools to improve analytical efficiency, process effectiveness, and risk-management outcomes.

Own the analytics roadmap for a Consumer Banking customer pillar, leading measurement, experimentation, and analyses across growth, retention, pricing, servicing, and strategy. Translate complex business questions into executive-ready recommendations, influence cross-functional decisions, establish analytical standards, and mentor analytics talent. The role requires strong statistical, experimental, communication, visualization, and stakeholder-management skills.

Summer intern supporting computer vision and multimodal AI research for medical imaging and video. Responsibilities include interviewing clinical stakeholders, identifying business value, developing and evaluating computer vision models, extracting insights from reports, and communicating technical findings. Work covers modalities including endoscopy, ultrasound, X-rays, dermatology images, retinal OCT, and histopathology.

Lead site reliability for liquidity risk applications, owning non-functional requirements. Drive resiliency, security, scalability, monitoring, automation, and enterprise-authorized AI adoption for reliability workflows. Influence strategy, run blameless post-mortems, coach engineers, hire and develop talent, and ensure traceability, auditability, and cross-team collaboration.

Leads enterprise release management operations, governance, audit readiness, cloud and platform modernization, and AI transformation initiatives. Oversees global teams, release execution across cloud and mainframe environments, ServiceNow and ChangeMan capabilities, CI/CD integration, automation, vendor management, compliance, and operational metrics. Partners with Engineering, DevOps, Security, Risk, Compliance, and Audit teams to improve release quality, delivery speed, resilience, and governance while developing team talent and transformation roadmaps.

Leads enterprise release and change management operations, governance, audit readiness, cloud modernization, and AI transformation initiatives. Oversees global teams, vendors, staffing, and strategic roadmaps while improving release quality, compliance, automation, deployment velocity, and operational resilience. Partners with Engineering, DevOps, Security, Risk, Compliance, and Audit teams to implement AI-enabled release intelligence, predictive risk assessment, automated workflows, CI/CD governance, ServiceNow improvements, and Mainframe ChangeMan modernization.

Entry-level technology associates develop expertise in software engineering, data engineering, or cybersecurity engineering. Responsibilities may include designing and maintaining software, building cloud-based data platforms and analytics solutions, or supporting global security, compliance, and troubleshooting initiatives. Participants receive technical training, business and leadership development, mentorship, and executive exposure while collaborating across teams and applying emerging technologies such as AI and machine learning.

Own and prioritize the product backlog, facilitate Agile ceremonies, write user stories and functional specifications, define acceptance criteria, and align engineering, design, and stakeholders. The Product Owner tracks delivery progress, oversees testing and validation, incorporates user feedback and performance metrics, and improves team workflows through clear documentation and proactive communication.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Own end-to-end model lifecycle, partner with product/engineering/operations/risk, improve data quality and feedback loops, monitor models in production, and optimize workflows to reduce agent effort and improve consistency.

Lead technical delivery for analytics-focused software: design, develop, test, and operate secure, high-quality production code. Drive automation and CI/CD, evaluate vendor/architecture choices, mentor junior engineers, and promote resilient cloud-native solutions within financial services technology teams.

Lead data ownership and governance to deliver curated, reusable data products. Partner across business, technology, analytics, and risk to define metadata, lineage, quality rules, and lifecycle management. Drive data integration, mitigate data risks, enable analytics/ML use cases, automate governance with AI/LLMs, and manage delivery, remediation, and stakeholder relationships to ensure scalable, discoverable, and enterprise-fit data products.

Lead strategy and execution for C360 Data Science Center of Excellence, driving AI/ML and modeling initiatives. Collaborate with Risk, Finance, Technology, and CDAO; manage global analytical teams; create executive-level presentations; align stakeholders and advance data-driven solutions including LLMs, semantic search and RAG for financial forecasting and insights.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.

Design, build, and operate production-grade GenAI and RAG platforms at scale. Develop retrieval pipelines, evaluation frameworks, lifecycle management, CI/CD, observability, safety controls, cost monitoring, and incident workflows. Partner with product, platform, and governance teams to meet enterprise reliability, security, and compliance standards. Mentor engineers and establish best practices for scalable, trustworthy AI systems.

Leads technology strategy, architecture, and application development for wealth investments supervisory platforms. Designs risk and compliance automation, scalable distributed systems, and end-to-end software solutions. Oversees development standards, system integration, resilience, disaster recovery, and regulatory controls. Guides teams through the full software lifecycle, mentors engineers, and collaborates with business and technology stakeholders. Drives AI, machine learning, blockchain, and asset-tokenization initiatives across alternative and mutual fund platforms.

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

Lead the design and development of high-performance trading systems using KDB/q, Python, AI, and machine learning. Build quantitative models, time-series analytics, research and backtesting frameworks, and real-time or batch production pipelines. Optimize systems for scalability, stability, and performance while collaborating with quants, traders, product owners, and engineers. Mentor developers, support production systems, and participate in on-call rotations.

Leads operational support for production AI solutions, including monitoring, incident management, defect remediation, enhancements, deployments, governance, and performance evaluation. Coordinates internal teams and third-party AI engineers, maintains support processes and documentation, and monitors reliability, accuracy, drift, bias, fairness, cost, and business value. Partners with data, technology, governance, and business stakeholders to ensure AI solutions remain stable, compliant, effective, and aligned with evolving needs.

The postdoctoral associate will design systems for transactive energy communities, focusing on socio-technical energy behavior and algorithm implementation for mechatronic systems.

Leads the design, architecture, and delivery of AI-enabled applications and digital initiatives. Evaluates generative AI, machine learning, robotic process automation, intelligent data processing, and predictive analytics use cases. Manages project scope, budgets, schedules, resources, quality, SLAs, and stakeholders. Translates business needs into technical solutions, drives operational excellence, and mentors teams on emerging AI technologies across financial services.

Build, deploy, and operate production AI applications and scalable machine learning solutions. Develop automated training and deployment workflows, support production AI infrastructure, and collaborate with software, data science, and platform engineering teams. The role involves Python development, ML frameworks, containerization, Kubernetes, Linux, and Git, with preferred experience in LLMs, RAG pipelines, model serving, vector databases, frontend development, and ML orchestration.