Software Engineer, Model Serving Infrastructure
The role involves contributing to the development of next-generation, high-performance machine learning serving systems. Responsibilities include building infrastructure that powers AI applications, working on problems at the intersection of distributed systems, machine learning, and high-performance computing, and solving fundamental computer science problems impacting AI deployment. Specific projects include implementing asynchronous inference for non-blocking client requests, designing intelligent request routing systems to balance load across thousands of model replicas with strict latency SLAs, building traffic management systems for zero-downtime model updates handling terabytes of inference requests, improving state management for scale from thousands to tens of thousands of replicas, architecting frameworks for multi-model orchestration in complex ML pipelines ensuring end-to-end latency guarantees, and developing observability and debugging tools for distributed ML applications at scale. The work involves writing performance-critical code in Python (with Cython optimizations) and potentially C++, working with distributed systems at scale using Ray Core's actor system, gRPC, and custom networking protocols, extending cloud-native infrastructure such as Kubernetes and service meshes, gaining system-level knowledge of ML/AI frameworks like TensorFlow, PyTorch, JAX, and transformers, and ensuring production reliability with tools like OpenTelemetry, Prometheus, distributed tracing, and chaos engineering to maintain 99.99% uptime. The role also involves leveraging AI coding agents to enhance team productivity while maintaining high code quality standards.
Parcel Contract Intelligence Consultant
Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.
Agentic Systems Engineer
Build agents as modular, plug-and-play components that slot cleanly into the wider stack. Add memory layers (short-term, long-term, summarization, retrieval-backed) into running systems. Wire up tool integrations, MCP servers, and skills. Own the quality of the features you put out, including tests, evals, and observability. Analyze production traces to understand system behavior and implement fixes accordingly.
Staff Software Engineer, Backend
As a Product Backend Engineer, you will design and operate backend systems that enable AI capabilities to provide dependable product experiences. Responsibilities include collaborating closely with Product to prioritize customer-focused work and deliver reliable features quickly, designing and owning backend services and APIs for web applications, workflows, and integrations, modeling and managing data in Postgres and related data stores, building secure, multi-tenant, permissions-aware systems with appropriate auditing, implementing backend features using LLMs and agentic tools, collaborating with frontend, product, and design teams to define API contracts and ship features end-to-end, adding logging, metrics, and tracing for service observability and on-call readiness, improving performance and scalability by profiling and tuning, and participating in code reviews, technical design discussions, and an on-call rotation for the services you own.
Senior Software Engineer, Backend
As a Product Backend Engineer, design and operate backend systems that enable AI capabilities to deliver seamless and dependable product experiences. Build secure, multi-tenant services, orchestrate interactions with large language models (LLMs) and agentic tools, and define backend architecture for expanding product offerings. Collaborate with Product to prioritize customer-focused work and deliver reliable features quickly. Design and own backend services and APIs supporting web applications, workflows, and integrations. Model and manage data in Postgres and related data stores to ensure low-latency and reliable user experiences. Build permissions-aware systems with appropriate auditing for enterprise and government customers. Implement backend features that interact with AI systems via robust, well-structured services. Collaborate with frontend, product, and design partners for solution design, API contract definition, and end-to-end feature delivery. Add logging, metrics, and tracing for service observability and on-call readiness. Improve performance and scalability by profiling, tuning, and refining service boundaries. Participate in code reviews, technical design discussions, and an on-call rotation for owned services.
Mathematics & Python Expert - Freelance AI Trainer
Design original computational mathematics problems that simulate real mathematical research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in areas including number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications from mathematical practice; verify solutions using Python with standard mathematical libraries; document problem statements clearly and provide verified correct answers.
Mathematics & Python Expert - Freelance AI Trainer
Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.
AI Deployment Engineer - Codex | APAC
The AI Deployment Engineer serves as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. They partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. The role involves building high-quality demos, reference implementations, and workflow automations using Codex as part of the development process. The engineer leads large-format workshops, technical deep dives, and hands-on enablement sessions to help engineering organizations adopt AI coding tools effectively and safely. They contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gathering high-fidelity product insights from real customer deployments, they translate these insights into clear product proposals and model feedback for internal teams. The engineer influences customer strategy and decision-making by framing how AI coding tools fit into their software development life cycle, technical roadmap, and organizational workflows. They also serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.
Optical Engineer - Freelance AI Trainer
Contributors are responsible for designing original optics problems that simulate real physics research workflows, ensuring the problems are computationally intensive and cannot be solved manually within reasonable timeframes, developing problems requiring non-trivial reasoning in mechanics, electromagnetism, thermodynamics, and quantum mechanics, basing problems on real research challenges or practical applications from optics and physics practice, and documenting problem statements clearly while providing verified correct answers.
Member of Technical Staff (Data): World Models
Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.
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