Top Remote Machine Learning Jobs in NYC, NY

Develop and post-train deep learning models while building systems, tooling, and workflows for training, evaluation, debugging, and deployment. Contribute to open-source projects, distributed AI infrastructure, backend services, and developer platforms. Collaborate with research, product, infrastructure, and customer teams to solve complex technical problems, prototype ideas, and productionize successful experiments.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

As a Senior Machine Learning Engineer in Model Risk Management, you will evaluate and validate models that assess risk in lending and fraud, ensuring their robustness and reliability. You will collaborate with model owners, tackle model challenges, and build evaluation tools critical for understanding model performance.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

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.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Design, implement, and deploy large-scale machine learning and optimization algorithms for ad ranking. Build and evaluate models, create online/offline evaluation frameworks, partner with product to define objectives and constraints, and communicate results to stakeholders to drive ad quality and performance.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Develop and test AI software using large datasets, algorithms, and statistical analysis for industrial applications. Collaborate with machine learning and data engineers to build integrated AI/ML solutions, evaluate performance, monitor industry trends and regulations, and present progress to leadership and stakeholders. The internship offers hands-on experience, mentorship, and exposure to practical AI innovation.

Develop and test AI software for industrial applications using Python, JavaScript, and C++. Evaluate data, statistics, and algorithms; collaborate with machine learning and data engineers; support end-to-end AI/ML integration; monitor industry trends and regulations; and present progress to leadership and stakeholders.

Design, develop, and maintain features for Gradio and Trackio, architect Python frameworks, collaborate with contributors, and enhance user experience for ML web apps.

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Leads an applied machine learning team and owns fraud detection and identity model strategy, development, deployment, monitoring, and iteration. Partners with Product, Engineering, Risk, and senior leadership to set priorities and deliver business impact. Provides technical mentorship, reviews code, guides modeling and architecture decisions, researches fraud patterns, develops ML capabilities, and shapes responsible AI use under data governance and security constraints.

Design and lead large-scale machine learning and backend systems for advertiser growth, including budget pacing algorithms, GenAI creative generation infrastructure, marketplace experimentation, high-throughput billing pipelines, and real-time stream processing. Analyze marketplace and financial data, optimize advertiser performance, ensure billing accuracy and fault tolerance, lead multi-quarter technical roadmaps, and mentor senior engineers.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.

Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.

Develop and productionize large-scale machine learning models and pipelines for Airbnb search, ranking, recommendation, and personalization. Build batch and real-time systems using structured and unstructured data, improve model serving and experimentation efficiency, and collaborate with engineering, product, operations, and data science teams. Drive technical decisions, quantify business impact, and lead multiple machine learning initiatives across teams.