Top Entry Level Machine Learning Jobs in NYC, NY

Research Engineer responsible for identifying weaknesses in voice AI models, defining improvement targets, designing evaluations, curating and generating data, fine-tuning deep learning models, and rapidly shipping production systems. The role emphasizes end-to-end ownership of customer problems, measurable production improvements, practical judgment, and fast experimentation. Candidates should have strong Python and PyTorch skills, production ML experience, and ideally experience with speech technologies, distributed training, publications, open source, or public benchmarks.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design, build, optimize, and scale machine learning models and systems for quantitative investment research and production. Collaborate with quantitative researchers and engineers on model architecture, distributed training, inference optimization, research tooling, and internal ML libraries. Improve training speed, scalability, reliability, performance, and cost efficiency across high-performance computing environments while translating research needs into robust technical solutions.

Own machine learning projects from scoping through production, developing reliable multi-agent AI systems. Design agent workflows, safety limits, memory and context management, model routing, evaluation pipelines, benchmarks, metrics, error monitoring, prompt structures, search and indexing systems, and document-processing validation layers. Conduct rigorous ML experiments, test systems end to end, write technical specifications, and communicate findings. The role is full-time and on-site five days per week in New York City.

Build machine learning infrastructure spanning large-scale data generation, distributed multi-GPU model training, training and inference optimization, and production LLM deployment and monitoring. The role also contributes to AI detection research and innovation. Candidates need strong Python and modern ML framework skills, deep learning experience, and knowledge of transformers and LLM fundamentals.

Build machine learning infrastructure spanning large-scale data generation, distributed multi-GPU training, model optimization, and production LLM inference. The role also contributes to ML research and innovation, requiring strong Python and modern ML framework skills, transformer and LLM knowledge, and comfort bridging research and engineering. Preferred experience includes CUDA, distributed training, vLLM, Spark, Beam, Airflow, MLOps, DevOps, and cloud infrastructure.

Designs and operates evaluation systems for evolving machine learning models. Responsibilities include defining metrics, building data and automated judging pipelines, analyzing model failures, simulating users and environments, running experiments, and documenting findings. The role requires production-quality Python, PyTorch, reinforcement learning evaluation or simulation experience, and strong machine learning research or open-source contributions.

Build shared machine learning capabilities for AI-native platform products, including document extraction, foundation models, model routing, evaluation, agentic systems, and continuous improvement. Translate urgent product-team needs into reusable platform abstractions, own capabilities through production, optimize accuracy, latency, cost, and reliability, and develop feedback and retraining loops that improve deployed systems across regulated institutional workflows.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Sets technical vision for Pinterest’s ML and media infrastructure, leading distributed training, fine-tuning, evaluation, inference, GPU optimization, content ingestion, and signal-processing architectures. Partners with AI/ML, Ads, Content, Shopping, and GenAI teams to productionize reliable, scalable platforms, drive architecture decisions and migrations, improve operational standards, and mentor senior engineers.

Build and scale pipelines that transform conversation data into structured customer memory. Develop LLM extraction and retrieval systems using embeddings, vector search, and ranking; create evaluation benchmarks and regression gates; diagnose production issues through logs, metrics, and traces; and partner with Product and ML teams to improve customer-focused AI systems.

Design, develop, evaluate, and refine machine learning models using Python. Manage training data with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and deliver data-driven insights. Collaborate remotely with cross-functional contributors and support deployment or operationalization of models in cloud or enterprise environments.

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.

Instructor-led AI and machine learning training covering theory, data preprocessing, algorithms, model building, analytics, real-time projects, case studies, and practical business and IT applications. Participants use Python, TensorFlow, and Scikit-learn and receive certification upon successful completion.

Develop physics-informed machine learning models for electronic systems. Build automated electrical, thermal, and electromagnetic simulation workflows, generate datasets, create surrogate models, and validate predictions against solvers and experiments. Develop active learning and optimization workflows while owning reproducible pipelines for data generation, training, evaluation, and deployment. Collaborate with hardware and machine learning teams to accelerate electronics development.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

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 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.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Build machine learning infrastructure spanning large-scale data generation, multi-GPU LLM training, training and inference optimization, and production deployment and monitoring. The role also contributes to AI detection research, publications, and technical innovation. Engineers will develop scalable data pipelines, manage distributed systems, deploy efficient LLM inference services, and collaborate across research and engineering teams in an in-person Brooklyn office.

Build and deploy production-grade AI systems for regulated institutions. Own ambiguous ML problems end-to-end, including data definition, model training, evaluation, deployment, and monitoring. Work with LLMs, vision models, reinforcement learning, agentic systems, and rule engines to process complex documents and support institutional decisions. Collaborate directly with customers, engineer for accuracy, latency, cost, and reliability, and develop rigorous evaluations and failure-mode taxonomies.

Develops personalized advertising recommendation algorithms using machine learning and deep learning. Responsibilities include identifying modeling opportunities, defining research roadmaps, developing and validating new models with large-scale user data and compute, analyzing metrics, and partnering with engineering and product teams to productionize research and deliver measurable business results.

Build and productionize machine learning systems that convert complex documents into executable, verifiable logic. Responsibilities include developing parsing and transformation pipelines, experimenting with language models, designing datasets and evaluation metrics, creating adversarial tests, analyzing model failures, and implementing rule-based checks using logical reasoning and program analysis. The role requires close collaboration with founders and subject-matter experts to validate system outputs.