Research and develop computer vision and deep learning algorithms for autonomous drones, taking ownership of projects from prototyping through product integration. The internship requires strong C++ or Python and PyTorch skills, mathematical foundations, and software engineering ability.

Build and operate production machine learning systems for ranking, retrieval, recommendations, personalization, and customer intelligence. The role requires 12+ years of production software and ML experience, strong expertise in intelligent systems, and sound judgment around trustworthy customer-impacting signals.

Senior engineer developing and productizing AI, machine learning, scientific computing, and data-analysis capabilities for a high-performance analytics engine. Requires 5+ years building quantitative data-intensive software and expertise in Python, machine learning, scalable architecture, and distributed computing.

Develop and productionize generative AI applications for customers while advising stakeholders and influencing product direction. The role requires extensive industry data science experience, production ML deployment expertise, graduate-level quantitative training or equivalent experience, and native Japanese with professional English.

Build and operate scalable AI/ML systems and pipelines that strengthen Airbnb’s fraud prevention and trust defenses. The role requires 7+ years of backend or platform engineering experience, strong programming and data engineering skills, and production machine learning expertise.

Leads a hands-on AI engineering team developing, evaluating, and deploying large-scale multimodal and video models. The role combines post-training, inference optimization, product experimentation, technical roadmap ownership, and people management.

Leads the engineering discipline for evaluating, testing, and monitoring production AI agents, while building scalable eval infrastructure and developer tooling. Requires 8+ years of production software experience, strong backend skills, and expertise with LLM evaluation and agentic systems.

Build and deploy production voice AI agents for customers, creating demos, debugging edge cases, improving performance, and translating feedback into product improvements. The role combines hands-on engineering, customer engagement, and pre- and post-sales delivery.

AI Engineering Intern building generative AI agents and RAG pipelines to automate internal engineering workflows and improve productivity. Requires Python, AI/ML and LLM project experience, Git familiarity, and current enrollment in a technical degree program.

Staff engineer responsible for designing and scaling the infrastructure, execution environments, verifiers, and tooling used to train and evaluate AI agents. Requires 8+ years of software engineering experience, strong Python and distributed-systems expertise, and familiarity with sandboxing, high-throughput systems, and LLM workflows.

Design and productionize ML, deep learning, and LLM models for personalization, recommendations, and search systems. Requires 7+ years building production ML/AI with business impact and strong cross-functional collaboration skills.

Build and operate infrastructure-first production ML systems, including pipelines, model serving, CI/CD, monitoring, and automated retraining. The role requires 5+ years of production ML experience, strong Python and platform engineering skills, and hands-on expertise with cloud, Kubernetes, and MLOps tooling.

Leads the architecture, development, and governance of agentic AI platforms and reusable patterns across SaaS products. The role requires principal-level experience with Python, Java, Azure and Anthropic AI technologies, RAG pipelines, model serving, evaluation frameworks, and production governance.

Staff AI Engineer responsible for architecting and delivering governed AI agents, enterprise applications, and integrations across Finance and business systems. The role requires 8+ years of production engineering experience, strong Python or TypeScript skills, enterprise architecture judgment, and familiarity with AI platforms, agent frameworks, and compliance controls.

Senior machine learning engineer owning cybersecurity detection capabilities end to end, from data pipelines and experimentation through production deployment and monitoring. Requires 3+ years of applied ML experience, strong Python and deep-learning skills, and demonstrated model delivery in customer-facing systems.

The Machine Learning Engineer will train and fine-tune models, build evaluation suites, analyze model failures, and improve dataset and training quality. The role requires at least two years of ML engineering or applied data science experience, strong Python skills, and practical model evaluation expertise.

Own and evolve Hugging Face’s open-source voice-agent stack and bring hf-voice from demo to production. The role requires senior experience with Python, distributed real-time systems, developer infrastructure, and production AI or multimodal models.

Build the AI platform behind fab2, including model infrastructure, agent systems, evaluations, and tools for engineering and fab operations. The role requires strong production software engineering skills and comfort working across frontend, backend, infrastructure, and data.

Develop and deploy real-time perception and sensor-fusion software for autonomous battery-electric rail vehicles. The role requires strong robotics, geometry-based computer vision, C/C++ and Rust experience, plus hands-on work with multimodal sensors and production systems.

Build replayable enterprise environments, evaluation systems, graders, and post-training workflows for AI agents. The role spans machine-learning research and production engineering and requires 1–7 years of software or ML systems experience.

Leads the design, implementation, integration, and field validation of tactical autonomy and multi-agent coordination capabilities for unmanned platforms. Requires 7+ years of relevant experience, production C++, technical leadership, and eligibility for a U.S. Secret clearance.

Design and deploy tactical autonomy algorithms and high-performance software for unmanned systems operating in complex, contested environments. The role requires 5+ years of related experience, strong C++ and Python skills, robotics expertise, and the ability to obtain a SECRET clearance.

Build and productionize generative AI applications for U.S. federal customers, advise clients, and influence product direction. The role requires extensive data science and machine learning deployment experience, a graduate quantitative degree or equivalent experience, and U.S. security clearance eligibility.

Build and scale production machine-learning pipelines and evaluation systems powering generative AI music experiences. The role requires hands-on LLM, prompt-engineering, data-pipeline, cloud, and user-facing product experience.

Build and operate distributed infrastructure and ML platform primitives that help researchers and product engineers move from experiments to trusted runs on large-scale compute. The role requires production systems experience, strong infrastructure skills, and close collaboration with ML research teams.

Leads the technical strategy and engineering execution required to achieve driverless freeway operation for autonomous vehicles. Requires 10+ years of software experience, demonstrated freeway autonomy leadership, deep expertise in an autonomy domain, and strong executive communication skills.

Build the technical foundation for a new business vertical, creating reusable infrastructure and leading early customer engagements from scoping through delivery. The role requires 3+ years of engineering experience, strong Python and SQL skills, backend/data expertise, and comfort operating in ambiguity.

Build production-grade AI agents, evaluation infrastructure, and developer tooling that make AI-assisted engineering faster, safer, and reusable across teams. The role requires software engineering experience, platform or internal developer-product experience, and hands-on expertise with LLM integration and orchestration.

Leads the roadmap and technical vision for Snowflake Feature Store, building reliable, high-performance machine learning platform capabilities and supporting technical execution across partner teams. Requires 10+ years of experience with data-serving infrastructure or ML platforms, plus Java and Python expertise.

Build and advance agentic machine-learning systems for multimodal creative tasks, with a focus on video understanding, reasoning, control, and tool use. The role requires strong production ML or agent-pipeline experience and deep knowledge of modern LLM techniques.

Build and operate scalable MLOps infrastructure for distributed training and inference across on-premises and GPU environments. The role partners with data science and AI teams and requires production experience with Python, Spark, Docker, Kubernetes, and open-source MLOps tooling.

Build modular AI operations and evaluation systems that power complex real estate workflows. The role focuses on improving output quality, defining correctness with domain experts, and reducing human review while maintaining high standards.

Build and operate Dougie, an agentic AI system that executes workflows, evaluates its own performance, retains institutional context, and improves in production. The role requires experience deploying unattended agentic systems and engineering reliable memory, retrieval, orchestration, and feedback loops.

Build and ship production agentic AI workflows for complex real estate and built-world processes. The role combines product engineering, applied AI, customer collaboration, workflow orchestration, evaluation, and reliable user-facing experiences.

Build, operate, and improve production LLM-backed agent systems while establishing rigorous evaluation, testing, monitoring, and code-review practices. This individual contributor role also coaches teammates and raises engineering standards across the AI and Data team.

Build and operate low-latency machine learning systems for ad ranking, relevance, and optimization, including feature pipelines, experimentation, evaluation, and production inference. The role requires 6+ years of software engineering experience, strong Python skills, AWS experience, and practical LLM application experience.

Build and operate machine learning models for sales roleplay, scoring, and coaching products, owning the lifecycle from fine-tuning and evaluation through production and on-device deployment. The role emphasizes open-source models, latency and privacy optimization, and rigorous model testing.

Conduct an end-to-end research project on speech and audio machine learning, training and evaluating models on large-scale telephony data and potentially advancing results toward production. Candidates should be pursuing a master’s or PhD or have equivalent research experience, with hands-on audio modeling and PyTorch expertise.

Build and ship backend and infrastructure capabilities across AI inference, model serving, and LLM fine-tuning for EMEA-specific needs. The role requires strong software engineering, Python and systems-language skills, production ownership, and first-principles problem solving.

Research Engineer responsible for operating and improving large-scale production pretraining systems, from performance optimization and hardware debugging to experiments, observability, and launch incident response. Requires deep ML systems expertise and experience with LLM training, JAX, TPU, PyTorch, or distributed systems.

Operates and improves the infrastructure powering large-scale post-training and reinforcement learning runs, partnering with researchers to debug failures, improve reliability, and automate recovery. Requires 4+ years operating distributed production systems and strong Python, Go, or C++ skills.

Build customer-facing agentic AI experiences for loyalty programs, combining LLM workflows, product engineering, personalization, and safety controls. The role requires 6+ years of software engineering experience and production experience with AI or LLM-powered systems.

Deploy and optimize frontier AI models for fast, reliable, real-time production serving at scale. The role requires production ML serving experience, GPU programming and inference optimization expertise, and the ability to diagnose bottlenecks across the serving stack.

Research Engineer designing post-training infrastructure and running controlled experiments to measure how datasets affect foundation-model behavior. Requires at least 2 years of ML or research engineering experience, strong Python, and hands-on experience with PyTorch, JAX, Ray, Slurm, and LLM post-training.

Research-focused engineer advancing agentic model capabilities across synthetic data, task environments, evaluations, training, and usability improvements. Requires strong Python engineering, deep learning framework experience, scalable distributed training skills, and scientific experimentation ability.

Build and deploy machine learning systems that apply economic theory, econometrics, and causal inference to marketplace problems. The role requires advanced training in economics, strong Python and data skills, and production ML experience for senior-level hires.

Build and operate production ML infrastructure spanning training, deployment, serving, monitoring, data pipelines, and feedback-driven retraining. The role requires strong MLOps and DevOps experience, Python and SQL proficiency, and ownership of reliable cloud-based systems.

Develop and deploy responsible AI and machine learning fairness solutions across Pinterest’s large-scale, user-facing products, including generative AI, search, and recommendations. The role requires production ML experience, expertise in fairness interventions and modern architectures, and a master’s or PhD in computer science or a related field.

Build and operate backend infrastructure for machine learning model training, serving, feature management, and marketplace simulation. The role requires 6+ years of software engineering experience, distributed systems expertise, and experience with production ML platforms.

Build and operate production machine-learning systems for content safety, from messy customer data through classification, evaluation, and inference. The role requires 5+ years of ML engineering experience, strong Python and MLOps skills, and sound judgment across classical models and LLMs.