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Lead a global data team at a fintech company building brokerage infrastructure, overseeing data strategy, analytics, and AI initiatives while managing partner invoicing and embedded analytics for financial institutions.
Build and maintain the AI platform infrastructure, connectors, and execution patterns that enable safe, scalable use of agentic AI systems across Alpaca's fintech brokerage APIs.
A principal-level engineer on Nscale's AI Infrastructure Operations team who defines how multi-thousand-GPU AI clusters are validated and tuned for production: building Python control-plane and burn-in tooling, running real distributed training/inference workloads, and debugging performance across GPUs, InfiniBand/RoCE fabrics, storage, and SLURM/Kubernetes schedulers.
Senior Product Manager who owns the observability toolset for ASAPP's GenerativeAgent: simulation scenario tools, evals/mistake monitoring, a config Previewer, and production Conversation Explorer. Day to day you define and ship testing, evaluation, and review products for LLM agents, partnering closely with research and engineering.
Owns the agentic tools platform that powers ZoomInfo's multi-agent orchestration system, defining tools, integrations, and standards for AI agents across products like Copilot and GTM Studio.
Product Designer at F2, an AI platform that turns unstructured deal materials into insights for private markets investors (private credit, banks, PE). Leads end-to-end design — research, prototyping, interaction design, UI — and builds design systems for a complex B2B AI product used by investment professionals.
Analyze data to drive insights for autonomous robots and automation products in last-mile logistics, using SQL, Python, and BI tools.
Owns the product that generates accurate delivery-time estimates for Wolt’s customers, using ML models and cross-functional collaboration to balance speed, reliability, and profitability.
Senior Test Engineer ensures software quality and reliability by embedding testing into the development lifecycle, designing automated frameworks (Playwright/Cypress), and collaborating with teams to deliver scalable, high-performance solutions for AI-driven enterprise products.
Lead a team of ML engineers building AI systems that help restaurant partners thrive on DoorDash, from onboarding to real-time order ops, using LLMs, generative AI, and agentic automation.
Lead AI-driven ranking and relevance systems for DoorDash’s ads marketplace, designing and deploying ML/LLM models to personalize ad delivery in real time across global markets.
Build and deploy large-scale ML systems that power real-time logistics decisions like delivery assignment and ETA estimation, shaping DoorDash’s fulfillment efficiency and cost.
Research and build AI agent systems to improve DoorDash’s logistics and user experiences, deploying solutions at real-world scale with reinforcement learning and multi-agent techniques.
Leads AI research at DoorDash, defining the research agenda, hiring, and culture for an org focused on deploying cutting-edge AI models in logistics and marketplace dynamics using proprietary data and compute resources.
Design statistical methods and standards for experimentation across DoorDash, Deliveroo, and Wolt, partnering with engineering to build the platform and decide where AI fits in the process.
Senior SRE building and operating CI/CD pipelines, Kubernetes clusters, and monitoring for AI-powered maritime defense systems using Terraform, Docker, and cloud platforms.
Senior distributed systems engineer building the runtime infrastructure behind Moveworks' AI agents at ServiceNow: agent orchestration engines, lease-based session management, event-driven pipelines, and observability. Day-to-day is backend/infra engineering in Python/Go with DynamoDB, Kafka, SQS, Redis, gRPC, and OpenTelemetry.
Senior AI Engineer builds and leads AI platforms for a healthcare data company, focusing on GenAI models, pipelines, and agentic systems to improve decision-making for health plans.
Senior ML engineer on Moveworks' ML infrastructure team (ServiceNow) who builds, optimizes, and scales end-to-end infrastructure for training, evaluating, and serving LLMs in production. Core technologies include PyTorch/Hugging Face, LLM serving frameworks like vLLM and TensorRT-LLM, Python plus C++/Go, and distributed training and inference pipelines.
Product manager at AI lab Thinking Machines Lab acting as a high-trust partner to post-training researchers on frontier models (Inkling, Tinker) — connecting research priorities, model behavior, evaluations, data/environments, infrastructure, safety and user learning into product decisions. Based in San Francisco, paying $350k–$450k.
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