Top Entry Level Machine Learning Jobs in NYC, NY

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.

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.

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.

Twelve-month rotational program embedding early-career ML engineers across two six-month deployments. Build, deploy, and monitor production ML models at scale using cloud ML platforms, work with large streaming datasets, integrate models into consumer and enterprise systems, and iterate on performance, latency, reliability, and drift detection in high-visibility 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.

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.

The Machine Learning Engineer will build data pipelines, manage multi-GPU training infrastructure, optimize code, and deploy inference pipelines for LLMs.

Design and evaluate next-generation recommendation and ranking models using LLMs, RLHF, and preference learning. Build user understanding systems for conversion and value estimation at scale. Apply RL and optimization to bidding and real-time delivery. Run causal inference, A/B tests, and offline evaluations. Collaborate with engineering to productionize research and communicate results to technical and non-technical stakeholders.

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.

Develop and productionize consumer-facing AI features using LLMs. Collaborate across the AI stack, run A/B tests and evaluations, improve model reliability and guardrails, and optimize latency, cost, and scalability of production AI systems.