Top Machine Learning Engineer Jobs in NYC, NY

The Lead Machine Learning Engineer designs and implements ML applications, builds data pipelines, collaborates with teams, and ensures performance and governance of ML systems.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Develop scalable ML platform infrastructure, support ML training and deployment, collaborate on software tools for scientists, and enhance workflows and automation.

This role involves building and scaling machine learning systems for content understanding, improving quality, and collaborating with cross-functional teams to enhance user experience on Spotify.

Build and deploy ML models and pipelines for entity resolution and a knowledge graph at scale. Fine-tune algorithms, expand data ingestion and enrichment, create evaluation harnesses, and integrate agentic workflows to support research and product teams processing tens of millions of records daily.

Build, deploy, and optimize production AI/ML and LLM-driven decisioning systems for claim offer generation and negotiation. Own end-to-end model development, evaluation, experimentation, and operational reliability while balancing fairness, business objectives, and product requirements.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

Develop and maintain ML pipelines for entity classification; collaborate with data scientists; implement monitoring frameworks and improve data workflows.

The role involves building and scaling machine learning systems for content moderation, designing evaluation frameworks, and developing multimodal models for policy enforcement, while mentoring other engineers.

The Machine Learning Engineer will design, develop, and implement advanced recommendation systems, collaborating with cross-functional teams to enhance personalization for a global audience.

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.

Design, build, productionize, and maintain large-scale ML models and infrastructure using structured and unstructured data. Perform feature engineering, scale models and systems, run experiments, collaborate with researchers, and ensure code quality through reviews.

Design, build, and deploy scalable GenAI/LLM-driven compliance solutions (RAG, embeddings, agentic frameworks). Lead projects, produce production-ready code, mentor engineers, collaborate with compliance and legal stakeholders, and ensure regulatory-aligned, monitored ML systems.

Early-career ML engineer who builds, evaluates, and improves LLM-powered financial applications. Design and implement ML models, test and analyze LLM failure modes, apply statistics and experimental design, collaborate with cross-functional teams, ensure responsible AI practices, document results, and support junior teammates.

Design, prototype, and productionize AI/ML systems (recommendations, NL interfaces, agentic workflows). Build and scale ML infrastructure, partner with product/design/engineering, and solve UX problems at the intersection of AI and BI.

Design, prototype, and productionize AI systems (recommendations, natural language interfaces, agents). Partner with product and engineering to identify AI opportunities, build scalable ML infrastructure, and manage the full ML lifecycle from data curation through training, deployment, and monitoring.

Design, implement, and optimize machine learning and deep learning models; preprocess and prepare large datasets; integrate models into production with software engineers; monitor and maintain production model performance; troubleshoot issues; and collaborate with stakeholders while staying current on ML/AI advances.

Lead research, design, and deployment of large-scale foundation models for machine-generated data (logs, time series, traces). Define AI strategy, mentor senior engineers, partner with product and exec teams, and drive production-grade model training, optimization, and MLOps for enterprise-scale applications.

Build and deploy autonomous AI agents and GenAI solutions at scale. Design and operationalize distributed data flows, agent orchestration, memory/RAG systems, and integrations with LLM APIs. Collaborate globally, prototype state-of-the-art ML/GenAI technologies, and ensure scalable, reliable cloud-native architectures.

As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.

Build systems for Mercor's hiring engine, including pipelines for indexing, sourcing, scoring, and conversion. Work with ML and backend technologies.

As an Open-Source Machine Learning Engineer at Hugging Face, you'll improve ML libraries, work with the community, and contribute to open-source projects.

Seeking a Machine Learning Engineer to apply AI technologies in high frequency trading, focusing on automation and collaboration with AI research.

Build and own LLM systems and infrastructure for Overtone's product: prompt architecture, evaluation pipelines, model experimentation, serving, fine-tuning (SFT/DPO/RLHF), vector DBs and personalization, observability tooling, and lightweight matching models. Collaborate cross-functionally to deploy reliable, auditable AI features in production.

Design, fine-tune, and deploy LLMs and RAG pipelines in private, secure environments. Build multi-agent systems, private inference infrastructure, evaluation frameworks, and observability tooling. Collaborate with platform teams to ensure secure, scalable production AI for enterprise customers.