Top AI & Machine Learning Jobs

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Lead the brand’s AI strategy, roadmap, use-case prioritization, and high-priority initiative delivery. Build business cases, develop prototypes, drive AI adoption and training, evaluate vendors and platforms, manage responsible-AI governance, and report portfolio value and risks to executives. The role partners across business, technology, legal, and parent-company teams to embed AI into workflows and deliver measurable results.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

Design, build, and deploy production-grade agentic AI systems for regulated banking use cases. Responsibilities include multi-agent orchestration, language-model fine-tuning, evaluation frameworks, MCP and A2A integrations, enterprise API development, cloud deployment, observability, and rapid prototyping. The role requires independently delivering tested, production-ready systems while collaborating with researchers, data scientists, and business stakeholders.

Build production-grade AI software, microservices, libraries, and data pipelines. Partner with data and backend teams to operationalize AI models, expose capabilities through APIs, deploy workloads using cloud infrastructure and Kubernetes, and maintain testing, code quality, and CI/CD standards. The role also involves ETL/ELT, database integration, and understanding AI concepts including inference, embeddings, LLMs, and RAG architectures.

Develop audience segments, customer profiles, and predictive models using behavioral, transactional, viewing, and digital data. Apply statistical and machine learning methods for clustering, churn, propensity, content affinity, and customer value analysis. Conduct exploratory analysis, evaluate models, support campaign measurement, and translate findings into business recommendations. Collaborate with Marketing, Product, Media Sales, and Content teams while presenting insights to technical and non-technical stakeholders.

Build and deploy machine learning models for supply chain, inventory, demand forecasting, pricing, promotion, clearance, and network optimization. Develop time series, predictive, simulation, and discrete optimization models; create scalable pipelines using Python, Spark, and cloud infrastructure; and own the full model lifecycle. Partner with engineering, product, and operations teams, communicate insights, develop decision-support tools, mentor junior data scientists, and measure business impact.

Own end-to-end data science initiatives across growth, personalization, pricing, market execution, risk, financial integrity, and financial crime. Build causal inference, predictive, ranking, and fraud detection models; deploy and monitor them in production; communicate uncertainty and insights to senior stakeholders; and mentor teammates on statistical and modeling best practices.

Build unified structured and unstructured data pipelines, predictive and prescriptive analytics, churn models with quantified uncertainty, and production LLM agents. Own enterprise AI architectures, RAG systems, MLOps and LLMOps standards, evaluation, monitoring, cost optimization, security, and reliability. Partner with business stakeholders to translate ambiguous problems into scalable AI solutions and document reference architectures adopted by other teams.

Build Syntro’s technical foundation across the stack, including systems that structure procurement data from emails, spreadsheets, ERP records, quotes, and supplier information. Design reliable AI agents that reason and act under uncertainty, establish scalable architecture and engineering standards, operate production systems, and work closely with customers. The role offers significant ownership over technical decisions, product direction, infrastructure, observability, and eventually the engineering team.