Top AI & Machine Learning Jobs

Develop and research novel machine learning algorithms for brain-computer interface applications. Improve brain activity identification, speed, accuracy, and muscle-artifact removal using real-time and large EEG datasets. Responsibilities include implementing and creating neural-network and other machine learning methods, rapidly prototyping features, collaborating with developers and the CEO, advancing intellectual property, documenting work, and contributing creatively in a fast-paced startup environment.

Build and shape an agent-orchestration platform using frontier LLMs. Transform heterogeneous enterprise program data into structured, queryable knowledge, develop reliable and observable backend services, and own features from design through production. Collaborate with product management and customers, make prioritization decisions, review code, write tests, and support production systems. The role requires strong Python backend skills, LLM production experience, cloud expertise, and comfort solving ambiguous startup problems.

Lead end-to-end data analysis and modeling initiatives (revenue forecasts, consumption, advertising, trends). Build and maintain production-level econometric and machine learning models, analyze large macro and industry datasets, create visualizations and analytical products, partner with Strategy and Finance to deliver insights, apply AI/tools, and mentor junior team members.

Leads projects in data science to analyze complex data sets using advanced statistical methods, providing insights for business decisions and managing cross-functional teams.

Build production-grade AI systems and multi-agent workflows that automate B2B trade processes, including collections, credit decisioning, payments operations, and other manual workflows. Develop reliable LLM-powered features with evaluations and guardrails, write clean performant code, collaborate with platform, product, and design teams, and help shape AI product and engineering practices in an early-stage environment.

Interns will design, build, test, and deploy AI-powered agents and developer productivity tools using pre-trained machine learning models and LLMs. Responsibilities include automating software development, testing, debugging, and quality assurance workflows; prototyping agentic systems; collaborating cross-functionally; participating in design reviews and code reviews; analyzing technical challenges; and presenting technical work.

Develop and pre-train large byte-native and multimodal foundation models. Implement architectures, training objectives, optimization methods, and stable scaling recipes. Build production-grade training infrastructure, run ablations, analyze training dynamics, and improve model quality, efficiency, reliability, and scalability across GPU-based distributed environments.

As a Senior Data Scientist, you will build predictive models for credit decisioning, analyze portfolio performance, and collaborate with teams to optimize loan strategies. This role focuses on driving credit quality and loss performance within the lending lifecycle.

Leads enterprise semantic layer, data, and AI platform architecture. Defines lakehouse, medallion, data model, data contract, metadata, schema governance, feature store, model lifecycle, and interoperability standards. Partners with product, engineering, platform, and data science teams to create secure, reusable foundations for analytics, machine learning, and AI applications. Translates business and governance requirements into scalable technical architectures and provides thought leadership across enterprise data and AI enablement.

Data Scientist Interns will automate processes, scrape and collect large datasets, and conduct advanced econometric analyses to investigate fraud across finance, healthcare, and cryptocurrency domains. The role involves applying data science and econometric techniques, contributing to individual and team projects, and translating academic expertise into practical industry applications.