Top AI & Machine Learning Jobs

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.

Leads AI governance and risk oversight across AI, generative AI, and automation initiatives. Responsibilities include designing controls, assessing and escalating risks, managing governance reviews, tracking remediation, developing risk metrics and executive reporting, and coordinating stakeholders across Legal, Compliance, Risk, Security, Technology, Data, and Operations. The role supports responsible AI deployment, regulatory examinations, audits, and cross-functional program execution in a regulated financial-services environment.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

AI Enablement Engineer who helps non-engineering Builders turn AI prototypes into secure, scalable, tested, observable, and maintainable production applications. Responsibilities include architecture guidance, technical coaching, code and security reviews, deployment, reusable templates and guardrails, production support models, and occasional end-to-end strategic builds. The role partners with engineering, security, and data teams while advancing AI application standards and Builder self-sufficiency.

Designs, builds, and scales AI-enabled tools, agentic workflows, analytical components, and data innovation frameworks for clinical, real-world, and operational data. Partners with scientists, engineers, product teams, and external collaborators to pilot emerging technologies, evaluate unconventional data sources, and support scientific decision-making. Provides innovation expertise and mentorship while ensuring traceability, human oversight, data privacy, security, and regulatory compliance.