Top Entry Level Machine Learning Jobs in NYC, NY

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

Lead Aaru’s Simulation Engineering team and own the quality, scalability, reproducibility, observability, and operational health of production AI simulations. Set architecture and productionization standards, build evaluation and regression systems, improve large-scale simulation performance, manage incidents and releases, collaborate across research and engineering, and recruit and develop high-performing engineers. The role remains hands-on, involving technical design, coding, debugging, and difficult tradeoffs among fidelity, calibration, latency, cost, and reliability.

Quantitative Researchers will build models and strategies for pricing and trading financial instruments, collaborating closely with teams and using various techniques from statistical methods to deep learning.

Build and operationalize LLM applications for financial services. Responsibilities include developing production AI features, Python libraries, data pipelines, prompts, tool-calling workflows, retrieval systems, evaluation suites, monitoring, and responsible-AI safeguards. Diagnose hallucination, latency, and cost issues; define metrics; analyze failures; collaborate with product, engineering, and business teams; and document designs and findings.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Develop and optimize machine learning and statistical models to predict liquid asset movements. Parse and analyze large datasets, implement academic research for alpha generation, and collaborate with Data, Alpha, and Strategy teams to design market-neutral trading strategies and run simulations to validate hypotheses.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Work alongside full-time ML researchers on end-to-end projects applying state-of-the-art models to large, nonstationary market datasets. Tasks include data exploration, model development, hyperparameter tuning, debugging training, and analyzing model predictions using large-scale compute and GPU resources. Internship-focused, with heavy emphasis on practical ML implementation and iteration.

Paid 3-month remote contract for students or recent grads to build and test LLM-based solutions using the Token Factory inference platform. Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization. Run performance and quality experiments and contribute to internal tooling and automation. Mentorship provided; strong performers may be considered for full-time roles.