Top AI & Machine Learning Jobs

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Lead and grow a software engineering team to design, build, and operate highly available, low-latency Responsible AI and AI service infrastructure. Drive architecture and code reviews, DevOps culture, cross-team program delivery, production readiness, and service KPIs while collaborating with engineers, scientists, and product managers.

Owns end-to-end product surfaces and production AI features, including LLM applications, prompt and evaluation pipelines, retrieval, agent workflows, reliability, and full-stack web and mobile development. The role requires substantial ownership, production AI experience, launch resilience, and technical leadership in a fast-paced, in-person environment.

Build scalable full-stack applications integrating AI and LLM technologies into web and mobile learning products. Responsibilities include experimenting with emerging AI tools, developing AI workflows, optimizing LLM applications, and collaborating with product, design, sales, and marketing teams. The role requires expert frontend, backend, database, cloud, infrastructure, and React Native experience, plus 5+ years of experience and startup or founding-engineer background.

Conduct applied AI research for education by reading papers, reproducing methods, training and evaluating models, building datasets and benchmarks, analyzing educational data, and developing research prototypes. The role also involves documenting experiments, collaborating with researchers and engineers, contributing to publications and open-source work, and assisting with RFP, grant, and research proposal development.

Lead end-to-end data science projects involving causal inference, experiments, statistical modeling, machine learning, political behavior, and online narratives. Build scalable data pipelines and production-quality Python code from messy datasets, rigorously evaluate models, collaborate with researchers and engineers, and contribute methods and technical direction. Mentor junior modelers while translating ambiguous questions into practical, deployable solutions.

The Data Scientist I will clean and structure messy datasets, engineer features, run exploratory and numerical analyses, fit and evaluate statistical and machine learning models, and support experiments. The role involves writing documented Python code, monitoring model performance, and collaborating with researchers and engineers. This early-career position offers mentorship while progressively increasing ownership of modeling projects involving online narratives, public opinion, AI, machine learning, and causal inference.

Tavern Research welcomes expressions of interest from curious, adaptable candidates interested in building technology at the intersection of data science, politics, and media production. Applicants should be comfortable solving difficult problems, learning quickly, using emerging tools, and performing under pressure. Political experience is helpful but not required. Employees are expected to work regularly in person from the company’s Chicago office.

Build and scale AI-powered marketing systems, including creative and landing-page generation, performance measurement, data integrations, and agentic workflows. Develop MCP servers and connect Claude with Snowflake, Braze, advertising platforms, GA4, and web systems. Own conversion optimization, attribution analysis, creator-program automation, experimentation, compliance guardrails, and multi-agent growth infrastructure. The role combines production software engineering, marketing automation, analytics, and rapid experimentation in a highly autonomous builder environment.

Build and ship AI-powered solutions for medical and clinic operations. Analyze clinical and operational data, develop LLM and RAG applications, design evaluation frameworks, integrate AI with business systems, and establish reliable, secure, auditable workflows. Partner with leaders, doctors, users, and engineering to identify problems, prototype solutions, measure adoption and impact, and scale successful products. The role requires hands-on ownership, product judgment, analytical rigor, and strong AI engineering expertise.

Research Scientist developing robot autonomy software and learning algorithms for dexterous manipulation in unstructured environments. Responsibilities include reinforcement and imitation learning, high-performance inference, production robot data pipelines, distributed policy training, edge deployment, and safety-critical software. The role also advances robotics research through novel methodologies while applying results to customer use cases.

Develop a modular robot autonomy stack combining Vision-Language-Action models with manipulation modules. Build safety layers, data pipelines, model-agnostic interfaces, and high-performance inference software for production robot fleets. Advance dexterous manipulation through reinforcement learning, imitation learning, and novel research while supporting real-world customer applications.

Lead the architecture and development of AI and NLP products that automate regulatory compliance for manufacturing companies. Own core models end to end, select technology stacks, guide experimentation and testing, balance speed with scalability, and collaborate with Product, Go-to-Market, Data, and customers to rapidly deliver production systems.

Design, develop, and maintain scalable backend systems for a composite AI platform (agent-based systems, LLM integrations, RAG). Own technical vision and roadmap, make architecture decisions, establish engineering best practices, provide senior technical leadership, and deliver high-impact production software.

Build and lead the applied science function for a market simulation product. Own scientific scoping, experimental design, interpretation standards, reproducible analysis, customer studies, and evidence quality. Partner with founders and deployment leaders to translate executive questions into defensible experiments and decision-ready insights. Establish research protocols, validation standards, engagement criteria, hiring plans, and spending priorities while remaining hands-on with quantitative research and customer delivery.

Lead simulation engineering for a planned, fundraising-dependent function. Build reproducible and inspectable simulation systems, define experiment execution and implementation standards, establish research-to-production workflows, improve quality, throughput, and cost, and create the hiring roadmap. Partner with causal research, evaluation, and infrastructure teams while balancing experimentation with production stability. This is a hands-on functional leadership role with equity-only compensation currently available and no guaranteed start date or funding.

Build and operate production AI platform capabilities, including agentic and multi-agent systems, evaluation and monitoring frameworks, fine-tuning workflows, inference optimization, safety controls, APIs, and rollback mechanisms. Partner with data and infrastructure teams to deliver reliable, scalable AI services while balancing latency, cost, GPU utilization, and operational risk. Senior engineers also mentor teammates, review designs, and raise engineering standards.

Lead engineering and deploy scalable AI agents and agent orchestration at scale. Prototype GenAI solutions, design and operationalize distributed batch/real-time data flows, implement memory/RAG and vector DBs, integrate tool-calling/APIs, build calibration and guardrails, drive cloud adoption, and collaborate with global teams.

Lead end-to-end design, development, and delivery of enterprise Generative AI and ML solutions. Architect scalable LLM, RAG, and agent-based systems, drive technical decisions, implement production-ready MLOps/CI-CD, and mentor engineers. Collaborate with product and business stakeholders, ensure monitoring, security, and operational support, and evaluate emerging AI technologies to improve delivery.