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Build and scale the shared infrastructure that powers DoorDash’s generative AI products, including real-time and batch LLM inference, fine-tuning, and agent platforms across San Francisco, Sunnyvale, and Seattle.
Build and deploy ML models for delivery ETAs, prep-time prediction, and logistics optimization at DoorDash Drive, using deep learning, reinforcement learning, and multimodal AI.
A Staff Machine Learning Engineer focuses on causal inference at DoorDash, building production systems for uplift models, counterfactual evaluation, and connecting experiments with ML to drive decisions across new verticals like grocery and retail.
Designs, builds, and productionizes causal ML systems (uplift models, heterogeneous treatment effect, counterfactual evaluation) for DoorDash's New Verticals (grocery, retail, etc.) to influence marketplace decisions like promotions and recommendations.
Build and deploy AI-driven simulation tools for engineering customers, focusing on 3D point-cloud/mesh data and ML pipelines in Python, while collaborating on-site with clients.
Wissen Technology is hiring an AI/ML Engineer in Bangalore (hybrid, 2-5 years experience) to design, build, and deploy Generative AI and Agentic AI applications using LLMs, integrating AI capabilities into enterprise workflows. Core focus areas include prompt engineering, RAG, vector databases, and production-grade AI solutions.
Lead the design and delivery of AI agent systems for a security operations platform, building scalable multi-agent architectures to process real-time machine data using LLMs and modern AI frameworks.
Senior ML Engineer at Air builds the LLMOps backbone of its AI-native Enterprise Readiness platform for government and industrial customers: fine-tuning infrastructure, dataset and evaluation pipelines, model serving, and observability. Heavy Python, Kubernetes, and GPU-cloud work; U.S. citizenship required; Pittsburgh office or remote.
Senior AI/ML engineer role in Singapore (onsite) focused on building Python-based ML applications: predictive models and time-series forecasting, scalable data pipelines on Databricks using PySpark and SQL, and REST services via FastAPI, Flask or Django, in collaboration with data scientists and engineers.
The Machine Learning Engineer will provide MLOps support for autonomous trucking model development, including debugging pipelines, maintaining infrastructure, and collaborating with engineering teams. The role focuses on ensuring reliable model deployment using Python, PyTorch, and various MLOps orchestration tools.
Build and improve ML models for personalized recommendations and ad targeting at a Korean local-commerce platform, using deep learning and LLM techniques.
Builds and serves ML models to personalize search results for a local-commerce app, using NLP, graph-based ranking, and real-time inference.
Build and maintain ML infrastructure—LLM routers, metadata systems, and model-serving pipelines—so product teams can ship AI features faster and more reliably.
Machine Learning Engineer bridging the sim2real gap for Gravis Robotics' autonomous construction machines: builds ML models of machine dynamics, defines validation metrics for model fidelity and sim2real transfer, and monitors performance changes over time. Core stack is Python, PyTorch, and git, with RL and system-identification experience valued.
A 12-month maternity leave cover role building and productionising machine learning models on local-authority data: designing training pipelines, LLM/RAG and embedding-based models, and containerised services deployed into Xantara's OneView platform. Core stack: Python, ML frameworks, SQL, Docker/FastAPI, with Azure cloud a bonus.
Owns the product roadmap for a machine learning platform that supports the full ML lifecycle, from data and feature readiness through model deployment and observability, while collaborating with data scientists, ML engineers, and platform teams.
Builds and maintains machine learning infrastructure for AB InBev’s B2B platform BEES, including training pipelines, inference services, and monitoring, using Python, PySpark, Kubernetes, and Azure cloud.
Senior Machine Learning Engineer at AB InBev Growth Group in Campinas, Brazil, building and scaling ML pipelines for the BEES B2B platform using Python, PySpark, Kubernetes, and Azure Cloud.
Build and scale ML systems for BEES, AB InBev’s B2B commerce platform, owning end-to-end pipelines from data to production inference.
Build and maintain ML platform components for a B2B e-commerce SaaS, including training pipelines, inference services, and observability, using Python, PySpark, Kubernetes, and Azure.
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