Top Remote Machine Learning Jobs in NYC, NY

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Serve as a technical partner to sales and customer success teams, advising high-value customers on cloud infrastructure and AI/ML solutions. Responsibilities include architecture reviews, proofs of concept, demos, onboarding, technical workshops, infrastructure optimization, LLM deployment and fine-tuning, GPU workload optimization, AI application design, data pipeline and database integration, escalations, documentation, and development of tools for premium customers. Collaborate globally with Engineering, Product, Support, and account teams.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Build and own fraud metrics, dashboards, and reporting. Design and implement real-time monitoring, alerts, and predictive ML-driven risk controls. Balance loss savings with customer friction, run experiments (A/B tests), partner with GTM teams, summarize findings to stakeholders, and assist teams with fraud triage.

Lead and grow a team of ML engineers building applications for a graphically intensive simulation platform. Manage stakeholders, translate customer needs into technical requirements, prioritize between off-the-shelf, open-source, and greenfield solutions, coordinate cross-functional delivery, and oversee the full ML lifecycle from data infrastructure to deployment. Foster team ways of working and performance.

Lead the technical strategy and hands-on development of scalable machine learning systems for Spotify’s Discovery Mode and royalty programs. Design, evaluate, deploy, and optimize production ML solutions that help artists reach fans. Provide architectural leadership, collaborate across product, engineering, research, design, and data science, prototype new approaches, improve system reliability, and mentor engineers.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

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.

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.

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 development of Root’s machine learning platform for insurance pricing. Build and operate feature pipelines, model training and orchestration, registries, validation, serving, observability, and reproducibility tooling. Partner with researchers to convert experimentation into reliable production systems, explore LLM-enabled data science automation, drive technical roadmaps and execution, and mentor engineers while maintaining high standards for reliability and correctness.

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 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.