Top AI & Machine Learning Jobs

Leads consumer personalization strategy and production development for recommendations, ranking, search, browse, carts, marketing, and agentic AI experiences. Builds recommender systems, learning-to-rank models, LLM agents, evaluation frameworks, and experimentation programs. Partners with Product and Engineering on deployment, monitoring, and model quality while mentoring senior data scientists, setting technical direction, shaping roadmaps, and communicating trade-offs to executives.

Build and operationalize production-grade image and video generative AI systems. Responsibilities include designing multimodal pipelines, agentic workflows, MCP servers, microservices, prompt management, model integrations, ML infrastructure, and scalable inference services. The role emphasizes reliability, cost, latency, observability, and collaboration with data scientists, designers, product managers, and creative teams to deliver AI-powered visual experiences.

Lead the architecture and development of MHC’s data platform, including ingestion, modeling, governance, pipelines, and feature stores. Build production AI and LLM capabilities such as document understanding, RAG, embeddings, evaluation, and intelligent product features. Manage and mentor a small data and AI engineering team, establish technical standards, define roadmaps, and partner with Product, Engineering, and business stakeholders.

Lead AI product strategy and deliver AI-powered features for a healthcare workforce SaaS. Translate AI concepts into user experiences, run discovery with customers, collaborate with data science and engineering, use LLM tools in daily workflows, define success metrics and experiments, ensure ethical AI practices, and evangelize AI capabilities internally and to customers.

Design and build agent-based AI systems, including single- and multi-agent architectures, tool-use workflows, feedback loops, planning, memory, delegation, evaluation, and autonomous processes. Improve reliability, observability, and failure recovery while exploring long-running and self-improving systems. Candidates should independently build and test projects, evaluate agent performance, follow current AI research, and understand how AI components interact.

Develop and productionize machine learning models for industrial process optimization using spectral and multimodal sensor data. Responsibilities include designing experiments, building preprocessing and feature extraction pipelines, monitoring model and sensor drift, detecting anomalies, developing ML infrastructure, and applying chemometrics, hybrid modeling, self-supervised learning, probabilistic reasoning, and time-series techniques.

Designs and delivers AI-enabled and automation-driven workforce productivity solutions. Partners with business leaders to identify opportunities, translate needs into requirements and workflows, build prototypes and production solutions, establish success measures, and implement responsible AI governance. Uses enterprise AI and automation platforms to improve productivity, operations, quality, service delivery, and revenue outcomes. Requires independent cross-functional collaboration, strong business judgment, solution design expertise, and experience with AI architecture, workflow automation, and organizational adoption.

Lead development of Avride’s ML infrastructure, initially owning GPU inference performance for onboard and simulation workloads. Build and optimize C++ execution frameworks, runtimes, and distributed infrastructure supporting neural network pipelines. Collaborate with applied ML teams on model architecture, diagnose performance issues, and expand ownership across broader ML pipelines. The role requires deep GPU and PyTorch knowledge, distributed systems experience, and strong expertise in multithreaded C++ environments.

Build and ship AI/ML products for finance and accounting workflows. Responsibilities include transforming messy financial data into production systems, designing evaluations, analyzing errors, optimizing quality and cost, and partnering with product and engineering. The role requires practical experience with machine learning, LLM applications, retrieval, embeddings, agentic systems, noisy data, and end-to-end production deployment.

Lead platform architecture and AI capability development for a regulated payment-integrity SaaS platform. Responsibilities include mapping current architecture, creating an AI roadmap, designing scalable multi-tenant systems, establishing engineering standards, delivering an AI-augmented MVP, and defining governance, evaluation, bias-testing, security, and data practices. The role provides technical leadership across engineering and data science while advising sales, operations, customers, and executives on practical AI adoption.