Top Senior Machine Learning Engineer Jobs in NYC, NY

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

Design, build, and scale distributed machine learning platform services for Roku’s personalized recommendations. Develop feature stores, real-time inference systems, vector databases, APIs, and platform infrastructure serving millions of transactions per second. Run online A/B tests, monitor system and business metrics, optimize recommendation performance, collaborate with cross-functional engineering teams, and mentor team members on platform engineering practices.

Lead development of an AI-ready research data ecosystem for vaccine R&D: design integrated data architectures, automate ingestion and transformation pipelines, define semantic/metadata frameworks, build knowledge graphs and RAG systems, and develop multimodal predictive and translational ML models. Provide technical leadership, mentor teams, and collaborate across scientists, bioinformatics, and enterprise data organizations to accelerate AI-driven vaccine discovery and decision-making.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

The role involves designing and deploying machine learning systems for content safety at Spotify, collaborating with various stakeholders, and providing technical leadership.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

The role involves designing and implementing large-scale ML solutions for personalized music experiences, collaborating with cross-functional teams to enhance user interactions and fulfill music recommendations.

Design, build, and deploy large-scale ML systems for ad ranking, bid optimization, and recommendations. Build and operate production ML pipelines (feature engineering, distributed training, deployment, retraining, monitoring) that process billions of events and serve low-latency predictions. Optimize training and inference for compute and cost efficiency. Partner with Data Science, Data Engineering, and product teams to ship models, run online experiments, and measure CTR/CVR/ROI impact. Incorporate applied ML research and techniques into advertising and audience modeling.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

The role involves building machine learning systems for Radar's products, focusing on core ML infrastructure, anomaly detection, geolocation, and collaboration with customers.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

As a Senior Machine Learning Engineer, you will build and operate scalable ML systems, oversee projects from inception to deployment, and improve system performance while collaborating with cross-functional teams and mentoring junior engineers.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

The Senior Machine Learning Engineer will design, deploy, and mentor on ML models, collaborate with customers, and influence product direction. Responsible for robust ML systems and data pipelines.

Lead the architecture and hands-on delivery of production AI systems using agentic frameworks, RAG, LLM orchestration, document intelligence, knowledge graphs, and evaluation pipelines. Manage and mentor 4–8 engineers, direct architecture and delivery, build Python and Java services, establish observability and CI/CD, and integrate AI platforms with enterprise systems. The role owns technical strategy for multi-agent workflows, vector retrieval, prompt engineering, validation, and AI-assisted developer tooling.

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.