Top Machine Learning Engineer Jobs in NYC, NY

Build, deploy, scale, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, and distributed ML systems using frameworks such as PyTorch and TensorFlow. Apply model validation, tuning, monitoring, responsible AI, testing, CI/CD, and deployment best practices while collaborating with Product, Data Science, and Agile engineering teams.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.

Design, build, deploy, retrain, and monitor production machine learning models and components. Develop scalable ML infrastructure, data pipelines, cloud architectures, and distributed systems using Python and related technologies. Collaborate with Product and Data Science teams, automate testing and deployment, apply CI/CD practices, and ensure model governance, security, responsible AI, and explainability across enterprise platforms.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Own and evolve Braze’s ML platform for training, deploying, serving, and monitoring models at global scale. Lead complex infrastructure initiatives, multi-region model serving, CI/CD, orchestration, reliability, cost optimization, and incident response. Set technical direction, improve production quality, collaborate across teams, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.

Design, build, and deploy scalable AI and ML solutions by wrangling and integrating data, implementing models (Python/Java, TensorFlow, Scikit-Learn), developing data pipelines and NLP applications, collaborating with clients, and mentoring junior team members to drive business insights and regulatory risk solutions.

Design, implement, and maintain production-oriented NLP and LLM models; build data pipelines and HPC analyses; integrate ML solutions with clinical research tools; ensure data quality, privacy compliance, and collaborate across multidisciplinary teams.

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.

Own the architecture and production delivery of a novel AI-powered exploitability engine. Lead probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drive zero-to-one technical direction, architecture, code reviews, cross-functional partnerships, and mentorship. Build scalable systems using distributed technologies, cloud-native development, APIs, databases, and machine learning while establishing defensible engineering decisions and evaluation metrics.

Develop and deploy machine learning systems powering AI agents for permitting workflows. Responsibilities include building LLM and NLP solutions, RAG and vector search systems, document-processing pipelines, scalable ML infrastructure, and production backend services. The role owns the full ML lifecycle, including experimentation, deployment, monitoring, evaluation, and iteration, while collaborating with engineering, product, and construction-domain experts.

Lead technical direction for Pinterest’s foundational machine learning capabilities in visual representation learning, multimodal content understanding, and LLM-based personalization. Drive initiatives from research and experimentation through productionization and adoption. Build reusable embeddings, models, semantic signals, and ML infrastructure using distributed training and data pipelines. Collaborate across product, engineering, data science, and applied science while mentoring technical teams and guiding multi-quarter roadmaps.

Develop and test machine learning models, pipelines, agentic systems, and LLM-powered applications under senior-engineer mentorship. Interns will work across the ML lifecycle, including problem framing, model selection, pipeline development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.

Lead the design and delivery of production machine learning systems for infrastructure observability, troubleshooting, and optimization. Develop anomaly detection, time-series analysis, event correlation, recommendations, ranking, and root-cause inference capabilities. Build evaluation frameworks, data pipelines, feature workflows, model-serving services, monitoring, and feedback loops. Integrate ML with telemetry platforms and customer-facing products, establish engineering standards, mentor engineers, and influence technical roadmaps across teams.

Own machine learning models end to end, from data preparation and training through evaluation and production deployment. Build pipelines for large multimodal datasets including video, sensor streams, and language. Collaborate with engineering and computer vision teams, rapidly prototype solutions, apply relevant research, and continuously improve production models and tooling.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Train and fine-tune diffusion models, adapters, and pipelines for character-based sticker generation. Build provenance-aware training data pipelines and evaluation systems measuring character fidelity and output quality. Partner with brand teams to secure character approvals, ensure reliable outputs from spoken requests, and collaborate with serving and trust-and-safety engineers to launch safe products for children.

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.

Build ML and data systems for climate policy analysis, renewable energy optimization, and scalable climate interventions. Responsibilities include developing LLM and NLP pipelines, knowledge graphs, predictive and recommendation models, causal analyses, evaluation frameworks, data visualizations, and reproducible MLOps pipelines. The role also maintains cloud data infrastructure, APIs, model deployment workflows, monitoring, and governance while collaborating with policy experts and community stakeholders.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Build and deploy production machine learning systems, primarily recommendation and personalization models. Own projects across problem definition, data pipelines, model training, deployment, monitoring, and optimization. Develop scalable low-latency inference infrastructure, run experiments and A/B tests, and apply ML to fraud detection, abuse prevention, and content moderation. Collaborate with engineering, product, and data science teams in a fast-paced, in-person startup environment.

Build and operate scalable machine learning training platforms and pipelines across AWS and other cloud environments. Optimize GPU-based single-node and distributed training for performance, cost, stability, and reproducibility. Develop Kubernetes infrastructure, GenAI and LLM fine-tuning workflows, observability, CI/CD automation, governance controls, and developer self-service tools. Partner with data engineering and platform teams while applying secure, responsible AI-assisted development practices.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

Lead design, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.