Top AI Research Jobs in NYC, NY

Leads Thomson Reuters’ applied AI and information retrieval research strategy, covering semantic search, ranking, RAG, agentic retrieval, NLP, knowledge graphs, and generative AI. Builds and manages multidisciplinary research and engineering teams, translates research into production systems, establishes evaluation frameworks, and partners with product and engineering leaders to deliver scalable cloud-native AI solutions and measurable business value.

Support consulting engagements and research across the semiconductor and AI stack by performing technical analysis, cross-stack research, analytical modeling, and preparing client-facing reports, presentations, and briefings. Track industry trends, evaluate hardware and AI model developments, and collaborate with senior consultants to translate technical insights into strategic recommendations.

Conduct applied quantitative research in global financial markets by analyzing large-scale market and order-flow data, developing predictive, statistical, stochastic, and machine learning models, testing hypotheses, and identifying trading opportunities. Researchers validate models in live market environments and collaborate with engineers and traders. The role includes structured graduate onboarding and requires a PhD in a quantitative STEM field, expected completion by mid-2027.

Mercor is offering $5 million in grants for research on AI capabilities, including real-world evaluations, interactive environments, long-horizon tasks, agentic systems, reasoning, post-training, and evaluation methodology. Academic groups, independent researchers, and nonprofits may apply with a focused proposal. Applicants should describe their team, background, research accomplishments, project, expected outputs, impact, timeline, and resource needs. Funded projects are expected to produce public research, datasets, methodologies, or tools.

Lead independent AI research on large language models and agentic systems, from hypothesis through training, evaluation, and production deployment. Develop post-training methods, reasoning and planning capabilities, evaluation benchmarks, synthetic data pipelines, and enterprise AI improvements. Publish at leading conferences, shape model strategy, and mentor researchers and engineers.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Develop novel machine learning methods and architectures for generative conversational speech-to-speech models. Research speech synthesis and recognition, improve model quality and realism, collaborate on data and infrastructure, and help scale proven research into production pipelines and Spotify products.

Leads ElectroKare’s scientific vision and machine learning research agenda for EKG waveform modeling and clinical prediction. Oversees ML engineers and researchers, guides sequence architecture decisions including Mamba, translates healthcare problems into ML solutions, partners on FDA validation, and represents the company’s science externally. Requires applied ML/DL research leadership, clinical or biomedical data experience, modern sequence modeling expertise, and advanced education or equivalent industry depth.

Lead the rapid development and release of AI safety and security benchmarks. Own evaluation taxonomies, harnesses, quality standards, publication decisions, research timelines, and quarterly roadmap planning. Collaborate with AI labs, universities, internal researchers, and freelance subject-matter experts. The role also requires reviewing research, engaging with frontier labs, presenting findings, and attending industry conferences.

Conduct postdoctoral research on AI agent-enabled autonomous experimentation for bio-inspired materials discovery. Develop trustworthy planning, reasoning, navigation, reinforcement learning, imitation learning, control, and probabilistic algorithms for single- and multi-agent systems. Build robotic laboratory workflows integrating liquid handlers, analytical instruments, cloud infrastructure, and AI agents. Publish research, collaborate with academic and industry partners, and mentor students and research staff.