Top Machine Learning Jobs in NYC, NY

Manage delivery of a portfolio of AI and data science projects from planning through implementation. Coordinate technical and non-technical teams, dependencies, requirements, budgets, timelines, and stakeholder expectations. Lead Agile ceremonies, drive accountability, translate AI capabilities into actionable requirements, and proactively identify and mitigate risks across complex, concurrent initiatives.

Lead and grow a team of data scientists for SoFi’s consumer finance businesses. Prioritize and deliver high-impact data science and ML projects, build data pipelines, deploy and monitor models in production, mentor engineers, recruit talent, and collaborate cross-functionally with product, engineering, finance, risk, and executives to drive data-informed business decisions.

Lead development and deployment of optimization and machine learning models to improve retail banking decisions. Translate business problems into mathematical formulations, build production-grade optimization and simulation models, collaborate with Product and Engineering to integrate solutions, and run end-to-end model development, evaluation, validation, and implementation.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Develop, validate, and maintain RMBS prepayment and credit/default models; perform large-scale data processing and ML analysis; build analytics platforms and monitoring tools; support valuation, hedging, risk assessment, model review, and client/internal training.

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.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Lead end-to-end improvements to recommendation systems and personalization. Partner with data science and engineering to set ranking inputs, run experiments, and measure impact while ensuring fairness, safety, and clear stakeholder communication.

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Leads a data science team and personally drives analyses, experimentation, and product analytics to identify patient experience improvements. Partners with Product, Engineering, Research, and Data Engineering teams to develop product features, evaluate business and product performance, guide product planning, and translate statistical findings into actionable recommendations. Manages and develops analytical talent while applying data storytelling, A/B testing, marketplace metrics, and retention, conversion, engagement, and churn analysis.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Lead development of machine learning and NLP solutions for operational risk analytics: partner with cross-functional teams, build end-to-end models (design, training, evaluation, validation, implementation), analyze large numeric and text datasets, deliver insights and reporting to senior leaders, and experiment with emerging generative AI techniques to improve risk management.

Drive market expansion and adoption of Kensho's AI products by bridging technical AI capabilities and commercial strategy. Lead go-to-market planning, customer demos, co-development partnerships, market analysis, sales enablement, and cross-functional alignment to translate customer needs into product and roadmap priorities.

Lead AI strategy, partner engagements, and operational execution across CrowdStrike products. Align R&D and product delivery to company AI strategy, prioritize initiatives, set organizational goals, drive thought leadership, and create order in complex cybersecurity AI environments while delivering customer value.

Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

Lead product strategy and lifecycle for Client360, driving discovery, roadmap, and launches. Partner with engineering, operations, and business teams to improve processes, measure KPIs, leverage analytics/AI for insights, manage stakeholders, and mentor junior staff to deliver scalable, compliant, customer-centric solutions.

Summer analyst will build and maintain quantitative models and algorithms for systematic trading and market-making, value and hedge transactions, conduct alpha research, calibrate models, optimize portfolios, and collaborate with traders, technologists, risk, and sales to deploy data-driven trading strategies.

Summer Associate will develop quantitative models and algorithms to support algorithmic trading, pricing, hedging, and portfolio optimization; collaborate with traders, technologists, and risk managers; conduct alpha research and deploy data-driven trading strategies.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.