Top Machine Learning Engineer Jobs in NYC, NY

Build and operate scalable ML-powered advertising measurement platforms (Brand Lift, Incrementality, Attribution). Develop distributed data processing systems, productionize models, collaborate with data scientists, and implement backend services and data pipelines to deliver privacy-safe, scientifically rigorous ad measurement.

Build and own ML infrastructure for underwriting: scalable training on tens of millions of patients, low-latency real-time inference for quoting, and tooling for faster data-science iteration including backtesting, validation, and experiment infrastructure. Ensure reliability, monitoring, SLAs, and on-call operations while collaborating with data scientists and actuaries.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

The Senior Lead Machine Learning Engineer will design, implement, and scale ML applications, ensuring performance and high availability while collaborating with cross-functional teams.

Lead technical design, development, and operational stability of AI/ML solutions on cloud. Drive adoption and governance of enterprise-authorized AI-assisted development, set validation standards, automate recurring remediation, evaluate vendors, and lead engineering communities of practice while coaching teams on responsible, secure AI usage.

Own the technical architecture and strategic direction of Disney’s News & Entertainment machine learning platform. Lead personalization, recommendation systems, ML infrastructure, data pipelines, model training, online serving, reliability, incident response, and platform standards across major media brands. Drive metrics-based business outcomes, cross-organizational engineering initiatives, technology adoption, architecture reviews, and mentorship of senior engineers.

Design and deliver AI/ML solutions for Business Banking: build NLP/LLM models, embed AI checks into campaign workflows, integrate data pipelines, automate quality metrics, and partner with cross-functional teams to operationalize and measure AI capabilities for marketing and campaign validation.

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 MLOps engineer to build and maintain distributed training pipelines, batch and real-time model serving, LLM quantization and deployment, vector DB optimization, monitoring/observability, and cross-team integration for production ML in AWS.

Lead design, build, deploy, and maintain production ML models and pipelines at scale. Collaborate with product and data science teams to architect cloud-based ML systems, automate CI/CD, monitor and retrain models, ensure model governance and explainability, and drive high availability and performance.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Build and maintain end-to-end machine learning infrastructure, including data and training pipelines, deployment workflows, monitoring systems, and supporting platforms. Partner with researchers and ML practitioners to productionize models, improve reliability, scalability, observability, reproducibility, speed, and cost efficiency, and automate operational workflows. This engineering-focused role supports reliable production ML systems rather than research-first development.

Lead design and implementation of end-to-end AI/ML solutions with emphasis on LLM fine-tuning/RAG, MLOps, cloud platform evaluation and setup (Azure/Oracle), model deployment and monitoring, and integrating model outputs into business reports (PowerBI/Oracle Analytics). Document processes and collaborate with cross-functional teams.

As a Machine Learning Engineer at Doppel, you will design, train, and deploy ML models for detecting malicious content, ensuring scalability and real-time performance while collaborating with teams and stakeholders.

Build and own production AI systems for legal workflows, including retrieval, document understanding, agentic LLM systems, evals, and data pipelines. Collaborate with engineers, lawyers, designers, and customers to translate legal judgment into reliable, usable product behavior.

The AI/ML Engineer will develop AI/ML solutions, drive business impact, and collaborate across teams to enhance profitability through advanced analytics.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

Design, build, and maintain scalable AI/ML architectures and systems. Collaborate with data scientists, engineers, product, and Compliance to integrate models into products. Evaluate tools and ensure performance, quality, and compliance while staying current with AI/ML advancements.

As a Machine Learning Engineer, you will design and deploy ML models, integrating LLMs with cultural intelligence and developing recommendation systems, while collaborating with data engineers and optimizing model performance.

Lead development of CustomerLake personalization using ML and LLMs: evaluate and improve models in production, build evaluation and optimization frameworks tied to business metrics, partner with product and design to deliver scalable MVPs, and set technical best practices for ML/AI personalization.

The Senior AI/ML Engineer will develop and improve analytical tools, approaches, and architecture for scalable AI and machine learning solutions, collaborating with engineers and data scientists on innovative projects.

Build and improve state-of-the-art agent systems: design RL/RLHF learning pipelines, create custom environments, set up robust evaluation, train smaller task-specific models, leverage open-source text and image models, and help shape technical and hiring strategy alongside founders.

As a Data Scientist at Mistplay, you'll design and optimize the rewards economy, implement machine learning systems, and analyze strategies to enhance user engagement and business outcomes.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead the design, development, optimization, and productionization of machine learning systems, particularly causal marketing mix modeling (cMMM). Build reusable statistical libraries and scalable ML pipelines, implement probabilistic techniques and agentic workflows, review designs and code, collaborate cross-functionally, and mentor ML engineers. The role requires expertise in production ML systems, statistical modeling, experimentation, and object-oriented programming.