Design and scale ML infrastructure and real-time learning systems powering personalization, search, ranking, and ad tech for millions of consumers. The role requires deep distributed-systems and data-pipeline expertise, strong architecture leadership, and experience delivering zero-to-one ML systems.

Build and ship production Applied AI capabilities, including agent infrastructure, RAG services, evaluation systems, and AI-powered engineering workflows. The role requires 6+ years of software engineering experience, strong backend and distributed-systems skills, and direct experience delivering LLM- or ML-powered products.

Build reproducible systems for AI model benchmarking, including datasets, evaluation pipelines, containerized environments, scoreboards, and analysis tools. The role requires 4+ years of professional engineering experience, strong Python, dataset rigor, and Docker expertise.

Build and advance multilingual language models through scalable ML systems, research, and large-scale data processing. The role requires deep NLP expertise, strong Python and software engineering skills, and a PhD or equivalent experience, with opportunities to publish research and mentor teammates.

Build and operate edge MLOps infrastructure for smart-camera machine-learning systems, including model deployment, TensorRT compilation, fleet updates, telemetry, and reliability. The role requires production MLOps experience, embedded inference optimization, and strong collaboration with data-science and embedded-engineering teams.

Build and operate AI-driven workflows, integrations, model-routing systems, and agent infrastructure across business and engineering functions. The role also supports model evaluation, AI cost optimization, governance safeguards, and organization-wide training.

Builds and productionizes machine learning systems for trust and safety, including abuse detection, autonomous AI agents, and evaluation frameworks. The role requires 5+ years of applied ML experience, strong Python skills, experience with LLMs and scalable pipelines, and a relevant advanced degree or equivalent background.

Architects and operates production machine-learning systems that classify web and API traffic, detect bots and scrapers, and support real-time mitigation at internet edge latency. The role requires 9+ years of applied ML experience in adversarial domains and strong expertise in evaluation, data pipelines, and large-scale systems.

Leads machine learning strategy and hands-on development for music promotion and royalty products, building scalable production systems and guiding complex technical initiatives. Requires deep machine learning expertise, production-scale implementation experience, and strong technical leadership.

Build and operate production machine learning systems for recommendations, search, advertising, content understanding, and LLM-powered experiences at internet scale. The role requires 3–5+ years of production ML experience, strong programming and software engineering fundamentals, and expertise with modern ML frameworks and scalable pipelines.

Design, build, and deploy production ML systems for recommendations, search, ranking, and advertising at internet scale. Own the full ML lifecycle from modeling to monitoring with strong cross-functional collaboration.

Build scalable software and tools for frontier model training, research experimentation, and production machine learning systems. The role requires strong Python and distributed-training expertise, experience with ML frameworks and infrastructure, and the ability to optimize and debug large language model systems.

Leads the technical vision, architecture, and roadmap for a company-wide machine learning platform supporting model training, deployment, serving, monitoring, and generative AI. The role requires expert Python and Java skills, large-scale MLOps experience, cloud and Kubernetes expertise, and organization-wide technical leadership.

Leads the technical vision, architecture, and engineering standards for a company-wide ML platform supporting model development, deployment, serving, and monitoring. The role requires principal-level expertise in Python and Java, scalable MLOps, cloud infrastructure, security, and technical leadership across teams.

Build and deploy production machine-learning models for fraud detection, identity verification, and financial risk products. The role suits new PhD graduates or early-career researchers with strong quantitative foundations, Python experience, and interest in owning the full ML lifecycle.

Build and operate large-scale machine learning infrastructure and models for Reddit’s recommendation and personalization systems. The role requires 5+ years of ML engineering experience, expertise in deep learning and distributed systems, and proficiency with Python and modern ML frameworks.

Leads the technical direction of large-scale ML infrastructure for embedding, recommendation, and personalization systems. The role requires 8+ years of ML engineering experience, expertise in deep learning and distributed training, and strong leadership across research, infrastructure, and production deployment.

Build and maintain tooling, evaluation systems, quality gates, and infrastructure for MongoDB's agent skills and AI platform. Requires 2+ years building production software, developer tools, CLIs, test infrastructure, or CI/CD, with strong fundamentals in API design, testing, and reasoning about nondeterministic AI systems.

Leads the technical direction and development of large-scale, GenAI-powered recommendation and feed-ranking systems. Requires 10+ years of industry experience in relevance-driven products, deep expertise in machine learning and recommendations, and strong organizational influence and mentoring skills.

Builds and owns production multi-agent AI infrastructure, backend integrations, and workflow automation for marketing operations. Requires 8+ years of software engineering experience, strong Python and JavaScript/Node.js skills, production LLM experience, and deep Google Cloud expertise.

Builds scalable experimentation, training, orchestration, and agentic AI infrastructure that accelerates Reddit’s Ads ML lifecycle. Requires 5+ years in infrastructure or distributed systems and production ML platform experience.

Leads the architecture and delivery of scalable machine learning infrastructure and customer-facing voice and audio generative AI solutions. The role requires extensive production ML experience, distributed systems expertise, cloud-native operations, and technical leadership across engineering teams.

Build, validate, deploy, and maintain machine and deep learning algorithms for biosignal and brain data used in medical devices and precision medicine. The role requires 4+ years of industry experience, production ML expertise, DSP and statistics knowledge, and proficiency with PyTorch or comparable frameworks.

Build and deploy machine learning models for fraud detection, identity verification, and financial risk products across the full lifecycle. The role targets new PhD graduates or early-career researchers with strong foundations in machine learning, statistics, Python, and production software development.

Build and deploy machine learning models for fraud detection, identity verification, and financial risk products across the full lifecycle. The role targets new PhD graduates or early-career researchers with quantitative training, Python experience, and strong analytical and communication skills.

Leads organization-wide AI evaluation and automation programs, establishing quality standards, metrics, production gates, and scalable evaluation infrastructure for LLM and agentic systems. The role requires 8+ years of relevant experience, strong technical systems expertise, and cross-functional leadership.

Leads engineering for Spotify’s Conversation Product Area, including shared agent infrastructure, evaluation systems, search, and voice platforms. The role manages approximately 70 engineers and requires deep technical leadership across large-scale ML platforms, agent systems, search, or speech infrastructure.

Leads development of scalable machine learning infrastructure, models, and internal platforms for voice and speech GenAI products. Requires extensive ML/AI experience, expertise in modern LLM techniques and audio models, distributed systems, cloud deployment, and production-scale data platforms.

Builds benchmarks, model evaluations, and backend infrastructure for an AI data platform’s Benchmarks and Evaluations vertical. The role requires 4+ years of engineering experience, hands-on model evaluation, and prior ownership of backend and infrastructure systems.

Build and scale AI-powered features for internet-scale security intelligence, including LLM and RAG systems, analytics, and workflow automation. The role requires 5+ years of software engineering experience, strong Python skills, and experience delivering secure, user-facing AI applications.

Build production LLM-powered systems for Instacart’s AI-for-Data platform, including retrieval, agentic workflows, evaluations, and internal data tools. The role requires strong Python backend engineering, SQL and data infrastructure experience, applied LLM expertise, and at least five years of professional experience.

Leads development of systems that optimize AI agents for cost, latency, and quality, with emphasis on agentic coding and reliable production software. Requires 8+ years of experience, strong concurrency and API design skills, and fluency in multiple programming languages.

Build and productionize large-scale generative speech models for voice conversion and related capabilities. The role combines research, data, evaluation, distributed training, performance optimization, and responsible deployment of speech systems.

Build and ship AI-powered features for incident detection, triage, resolution, and observability workflows. The role requires strong production software engineering experience, practical LLM and GenAI expertise, cloud-native exposure, and a pragmatic approach to rapid experimentation.

Leads a team of machine learning researchers and engineers developing web-scale recommendation systems, guiding strategy, research-to-production execution, and cross-functional delivery. Requires 7+ years of post-graduate academic and industry experience, 3+ years of people management, advanced education, and strong ML publication credentials.

Conduct applied AI research by designing, evaluating, and optimizing machine-learning models and code, with a focus on PyTorch, GPU performance, and generative AI systems. Requires a PhD or postdoctoral degree and 1–3+ years of ML engineering or data science experience.

Leads the architecture and operation of a multi-tenant AI/ML platform supporting production models, LLMs, and agents in regulated scientific environments. Requires 10+ years in distributed cloud-native systems, strong TypeScript and Python skills, production LLM/RAG experience, and technical leadership.

Build and deploy cutting-edge Agentic AI and LLM systems to transform Airbnb's customer service experience, including Chat and Voice AI assistants. Requires 6+ years experience with production ML/AI systems at scale.

Build and own the production execution and evaluation platform for Supabase's internal AI agents and operating system. Enforce governance through code with risk tiers, durable state, human gates, and comprehensive observability on GCP with Python.

Build and own AI-assisted engineering tools, conventions, context layers, and evaluation frameworks inside a large Rails monolith to scale AI usage across engineering, product, and design teams. Requires 7+ years production software experience with recent Rails and LLM-backed system expertise.

Serve as technical SME driving adoption of agentic AI platform across departments. Build AI observability dashboards, govern vendor risks, integrate AI systems, and create repeatable IT workflows for safe, scalable generative AI use.

Technical Lead owning architecture, execution, and evolution of an AI-driven telehealth platform built on GCP. Hands-on player-coach role integrating production LLMs, leading a small senior team, and driving scalable distributed systems in a healthcare setting.

Staff ML Engineer on Spotify's Personalization team, owning ML models and systems for the Home feed and Shortcuts experience. Build and productionize personalized recommendation systems and agentic AI experiences using LLMs, PyTorch, and large-scale infrastructure; drive experimentation, optimization, and technical direction while mentoring engineers.

Build state-of-the-art end-to-end speech and audio generation systems with a focus on joint audio-video modeling. Own audio representations (VAEs, neural codecs), generative backbones (diffusion/flow-matching transformers), conditioning, alignment for voice cloning and sync with video, plus data flywheel, evaluation, and inference optimization for large-scale multimodal models.

Build and ship AI-powered analytics agents, LLM-driven workflows, and intelligence features for Databricks' internal GTM platform (Customer Zero). Requires 7+ years software/AI engineering experience, strong Python/SQL, hands-on LLM/RAG/agents experience, and fluency with AI coding tools like Claude.

Senior Data/ML Engineer owning end-to-end data pipelines, feature platforms, and production ML models that power Sardine's real-time fraud, KYC, and compliance decisions. Requires 8+ years building production data and ML systems with deep Python, distributed frameworks, GCP cloud stack, and fraud/risk domain knowledge.

Builds production-grade machine learning services and orchestration for conversational AI, coordinating vendor and internal LLM systems. Requires at least two years of ML/software engineering experience, strong Python skills, and familiarity with modern AI architectures and tooling.

Senior Software Engineer building automated evaluation pipelines, test infrastructure, and monitoring systems to validate quality of Deepgram's speech, audio, LLM, and multimodal AI models before production release. Requires 5+ years building test/evaluation frameworks, strong analytical skills, and backend experience in Python/Rust/Go.

Build and scale inference infrastructure for generative audio models including TTS, voice conversion, and ASR. Design high-performance, low-latency serving systems using Kubernetes, CI/CD, and GPU optimization to bridge research and production.

Build and ship AI agent orchestration workflows, integrations, and shared infrastructure that power automated growth across paid media, lifecycle, organic, CRO, incentives and more at Kraken. Requires 3+ years building agentic systems or automation with LLM APIs, strong API integration skills, and growth/marketing context.