Top AI & Machine Learning Jobs

Build and maintain production backend systems for a real-time learning AI engine: APIs, data pipelines, performance optimization, testing, CI/CD, and troubleshooting. Collaborate with AI researchers, prototype features, and operate LLM instances (Claude) at scale.

Build and deploy scalable AI-powered web applications and Generative AI workflows. Develop responsive frontend experiences, backend services, AI data pipelines, LLM integrations, and cloud infrastructure. Collaborate with product managers, designers, and AI/ML engineers to translate user needs into product features, optimize application and model performance, and manage cloud deployments. The role requires ownership of production-grade solutions using modern frontend frameworks, backend languages, RAG architectures, vector databases, CI/CD, and AWS.

Develop highly efficient deep learning and computer vision algorithms for edge applications, including self-driving cars and humanoid robots. Responsibilities include designing, training, and optimizing Transformer models, researching multimodal large language models and embodied AI, and building vision systems. The role also involves applying computer vision methods such as object detection, tracking, segmentation, and vision-language modeling, alongside robot control and sensor integration using ROS 2.

Develop statistical, physical, and atmospheric models for detecting, attributing, localizing, and quantifying trace gases and emissions. Analyze time-series, geospatial, sensor, and scientific datasets; design experiments; quantify uncertainty; and translate research models into production cloud-platform components. Collaborate with analytics, product, engineering, and customer success teams while documenting and communicating findings to technical and non-technical stakeholders.

Leads and mentors an AI software engineering team while serving as a player-coach. Oversees cloud-native AI development across requirements, design, implementation, testing, and AWS deployment. Drives enterprise AI adoption, integrates emerging models, manages resources and delivery, promotes engineering quality and operational excellence, and communicates project status, risks, model performance, and cloud costs to stakeholders. Contributes to technology strategy, roadmaps, capacity planning, automation, and process improvements.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.

Architect and build Imperative Care’s modern semantic data platform, knowledge graph, and enterprise AI foundation. Develop production generative AI agents, RAG solutions, intelligent workflows, and system integrations. Establish data governance, security, lineage, quality, and stewardship controls for a regulated environment. Enable self-service analytics and reporting while partnering with business, technology, vendors, and managed-service providers. Serve as an AI thought leader, drive adoption, and contribute to hiring and mentoring as capabilities expand.

Build developer tooling, MLOps capabilities, CLIs, SDKs, APIs, observability features, and highly available services for distributed AI workloads on Ray. Own projects end to end across data preparation, model fine-tuning, evaluation, serving, deployment, debugging, and optimization. Collaborate with users, field teams, and machine learning and distributed-systems experts to deliver intuitive, scalable developer experiences.

Build and operate production systems for agentic development experiences, including agent execution, tool use, context management, streaming, integrations, and long-running workflows. Extend provider-agnostic model architecture and connect agents with workspaces, tools, and developer infrastructure. Own projects through rollout, collaborate with Product and Design, participate in reviews, and improve system reliability, performance, and operability.

Works with business stakeholders to identify data-driven opportunities, analyze trends, and support business decisions. Designs data models, machine-learning algorithms, and predictive analytics models; preprocesses structured and unstructured data; identifies data sources and automates collection; analyzes large datasets for patterns; and presents insights through data visualization.