Top AI Engineer Jobs

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, optimizing data processing for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects through continuous improvement. The role requires strong backend software engineering skills and experience with production systems, with preferred experience in web crawling, distributed processing, cloud infrastructure, Terraform, and ML training data.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. Own engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, Python and Bash proficiency, and independent problem-solving in an ambiguous environment.

Build and operate large-scale data pipelines that collect, process, and improve audio datasets for AI model training. Discover new data sources, optimize data quality, throughput, and cost, and collaborate with AI scientists on next-generation models. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, optimizing processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, Python and Bash proficiency, production systems experience, and independent problem-solving.

Build and operate large-scale audio data pipelines for AI model training. Discover data sources, improve processing systems for quality, throughput, and cost, and collaborate with AI scientists on training datasets. Own engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, and proficiency in Python, Bash, and Linux.

Build scalable, reliable pipelines for acquiring and processing audio data used in AI model training. Improve dataset quality, throughput, and cost at petabyte scale; collaborate with AI scientists on training data and next-generation models; and own engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, processing petabyte-scale datasets, optimizing quality, throughput, and cost, and collaborating with AI scientists. Responsibilities include owning engineering projects end to end and improving production data systems. Required experience includes backend software engineering, Python, Bash, Linux, production systems, and large-scale data processing.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, optimizing data processing for quality, throughput, and cost, partnering with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable ingestion and processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists on next-generation models. The role owns engineering projects from discovery through implementation and continuous improvement, using backend engineering, data processing, and cloud infrastructure expertise.

Build and operate large-scale data pipelines that acquire, process, and optimize audio datasets for AI model training. Responsibilities include discovering data sources, improving data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills and experience with production systems, with preferred experience in cloud infrastructure, Terraform, web crawling, and distributed data processing.

Lead development of agentic AI platform capabilities, including orchestration frameworks, RAG systems, tool calling, memory management, APIs, and production deployment patterns. Drive architecture, platform roadmaps, reliability, monitoring, governance, privacy, model risk controls, and responsible AI practices. Partner with product, engineering, risk, and control teams while coaching engineers on secure AI-assisted development and communicating technical strategy to senior stakeholders.

Lead development of agentic AI platform capabilities, including orchestration frameworks, retrieval systems, APIs, SDKs, reference implementations, memory management, tool calling, and production deployment. Define platform roadmaps, architecture, reliability, monitoring, testing, governance, privacy, and model-risk standards. Partner with product, engineering, risk, and control stakeholders while guiding responsible AI-assisted software development and communicating technical strategy to senior leaders.

Build, test, document, and improve Python applications, agents, and AI-assisted workflows using enterprise data and AI platforms. Process data, automate tasks, connect APIs and models, evaluate AI results, create tests, and debug solutions. Collaborate with data science, engineering, IT, security, and business teams while following version-control, privacy, documentation, and code-review practices. The role is early-career and emphasizes applying existing AI capabilities rather than training models from scratch.

Build and deploy production software and AI services, including APIs, UIs, ML pipelines, and agentic evaluators. Design scalable cloud architectures, maintain full-stack deployments, and apply AI technologies to solve complex customer problems. Collaborate with engineers, product teams, customers, partners, and executives to gather requirements, deliver solutions, and drive consensus. The role also involves prototyping, shipping releases, field debugging, stakeholder management, mentoring, and continuous learning.

Conduct applied AI and machine learning research spanning multimodal intelligence, recommendation, ranking, prediction, foundation models, agents, and decision-making. Define research problems, evaluate and develop models using proprietary data, run rigorous experiments, create training signals and evaluations, and transition successful approaches into production systems. Collaborate with engineering and product teams while shaping the company’s research agenda and technical standards.

Designs, builds, deploys, and supports production agentic and LLM-powered solutions and reusable AI platform services. Establishes secure integration patterns, guardrails, observability, evaluation, and operational practices for enterprise AI. Responsibilities include AI security, model and workflow optimization, automated testing, CI/CD, production support, enterprise integrations, standards development, emerging technology evaluation, stakeholder collaboration, and mentoring engineers.

Build customer-facing AI demos, agents, reference implementations, integrations, and reusable tooling for Claude capabilities. Own demo readiness through environments, evaluations, reliability checks, synthetic data, and fallback paths. Partner with Product, Engineering, Marketing, Operations, sales, and solutions teams to support launches, automate demo workflows, improve quality, and translate customer insights into product priorities.

Supports stakeholders in applying Generative AI by designing and refining prompts, developing AI-enabled workflows, validating outputs, documenting use cases, researching emerging capabilities, and assisting with demonstrations, training, adoption, and implementation. The role translates business needs into practical AI solutions that improve mission and operational efficiency.

Design, build, and operate high-performance, cloud-native distributed services supporting machine learning and data platforms. Lead Java and Spring Boot development, AWS architecture, infrastructure as code, observability, reliability improvements, and production troubleshooting. Translate ambiguous requirements into robust service designs, influence engineering standards, lead technical reviews, mentor engineers, and promote responsible AI-assisted development practices.

Designs and operates production-grade Python services, APIs, distributed systems, and cloud-native applications for enterprise machine learning platforms. The role uses AWS, infrastructure as code, observability tools, automated testing, CI/CD, and secure deployment practices. Responsibilities include architectural guidance, code reviews, mentoring, production support, resiliency engineering, and collaboration with product, platform, and site reliability teams. The engineer also helps adopt AI-assisted development practices and may build solutions using generative AI and large language models.

Develops research methods for context discovery, retrieval, multimodal representation, reasoning, tool calling, metadata management, and trustworthy AI data pipelines. Builds and evaluates foundation models across language, vision, time series, and scientific applications. The role combines applied research with hands-on engineering, including MLOps, agentic AI, knowledge-infused learning, pipeline optimization, and collaboration with software teams and external partners.

Designs and leads AI Quality Engineering for enterprise AI applications, LLMs, intelligent automation, and agentic systems. Responsibilities include building validation and testing frameworks, prompt and workflow testing, runtime quality controls, observability, telemetry, drift detection, reliability support, and release validation. The role collaborates across engineering, security, governance, clinical, data, and product teams to validate healthcare workflows and responsible AI deployments, while mentoring quality engineers and advancing AI-enabled testing practices.

Lead full‑stack design, development, and maintenance of scalable financial applications across front-end, back-end, and databases. Build RESTful APIs and microservices, participate in code reviews, troubleshoot production issues, mentor junior developers, and adopt AI/LLM tools and cloud/container platforms to improve delivery and security.