Top AI & Machine Learning Jobs

Build scalable platforms and pipelines that transform real-world, vendor, tutor, acquired, and synthetic data into realistic reinforcement-learning environments and tasks. Develop self-service APIs, tooling, automated quality checks, and data validation layers for internal and external contributors. Partner with research and ML platform teams to translate model capabilities into effective training environments and ensure trustworthy data production at scale.

Build and productionize AI features and tooling: LLM API integrations, RAG pipelines, observability, prompt/context management, and AI-driven automation. Prototype solutions, harden for production, write tests, and collaborate with Product, Engineering, and Data to scale reliable, cost-effective AI capabilities across the business.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Design and implement evaluations for AI agents across voice, vision, and browser use. Build benchmarks, simulations, and scoring systems for customer-specific scenarios, then use evaluation findings to improve agent behavior, context, and underlying models.

Leads enterprise data science, analytics, AI, and GenAI strategy across products and platforms. Owns production system quality, data integrity, explainability, validation, monitoring, and audit readiness in regulated environments. Partners with Product, Engineering, IT, and business leaders to deliver scalable solutions, data monetization, and strategic integrations. Builds and mentors senior data science talent while ensuring commercially viable, risk-aware outcomes.

Own production-grade analytics pipelines from design through maintenance, build dbt-based analytical models using Kimball dimensional modeling, and deliver reliable data features. Partner with Product, Engineering, and Data Science teams to translate retail signals into business insights. Establish standards for monitoring, documentation, reproducibility, and code quality. Use AI tools to accelerate development and prototype conversational data experiences for natural-language retail data exploration.

Provides technical leadership in data science and AML data governance. Translates business and financial-crime risk problems into scalable data solutions using Python, SQL, statistics, advanced analytics, data pipelines, and visualization. Partners with business, engineering, and analytics teams; oversees regulatory reviews, data controls, risk remediation, and governance initiatives. Communicates complex insights to senior stakeholders, supports enterprise programs, mentors colleagues, and drives continuous process improvements.

Designs, develops, and deploys enterprise Generative AI solutions using LLMs, semantic search, and AI orchestration. Builds monitoring systems, improves AI architecture, supports prompt engineering and evaluation, reviews code, and mentors engineers. Collaborates with product leaders and stakeholders to deliver secure, scalable, customer-facing AI applications. Requires Azure OpenAI, Azure AI Search, Semantic Kernel, DevOps, CI/CD, testing, and advanced Python expertise.

Build and own Custral’s AI agent runtime and full-stack product surfaces. Develop tool gating, context assembly from Postgres and OpenSearch, model selection, tracing, evaluation harnesses, and regression suites for LLM systems. Ship React, Node.js, and Express functionality to real users, improve agent capabilities, and ensure model and prompt changes deliver measurable customer impact. Work independently in an early-stage, in-person New York City environment.

Conduct a semester-long applied AI research project with an AfterQuery mentor in areas such as data quality, post-training, agent and foundation-model evaluation, or public benchmarks. Responsibilities include formulating research questions, designing experiments, developing datasets and evaluation methods, analyzing results, documenting reproducible work, and co-authoring a publication. Scholars receive mentorship, engineering support, annotation resources, and compute access.