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We're building the infrastructure layer for agentic web interaction at scale. Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help developers build agents that are not only intelligent - but also informed.

We work with some of the most innovative teams in AI - from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.

The Role: DevOps Engineer
Managing Kubernetes clusters across multiple environments and regions

Owning infrastructure as code for all resources

Maintaining and improving CI/CD pipelines and GitOps-based deployments

Maintaining and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processors

Building out monitoring, alerting, and observability

Debugging production issues across services

Managing cloud costs and capacity planning

Working closely with a small engineering team - you'd own infra, not a slice of it

We are seeking a high-performing GTM Engineer to join our team as a strategic force multiplier for our sales and marketing engines.

In this role, you will work closely with our GTM Engineering Lead to accelerate the transition from "old school SalesOps" to a modern, AI-driven Go-To-Market infrastructure.

This is an AI programming job in the field of Go-to-Market.

You will be a key developer and builder behind our AI-driven growth, turning strategy into high-performing code, autonomous agents, and scalable workflows.

Key Responsibilities

Identify high-leverage opportunities to automate manual workflows across Marketing and Sales using LLMs, autonomous agents, and modern API integrations.
Work closely with the GTM Engineering Lead to translate strategic growth initiatives into functional, AI-powered internal tools.
Design and build custom data pipelines and automations that remove friction for SDR and Marketing teams, allowing them to work smarter and move faster.
Own the development lifecycle of internal growth tools-from building and testing to shipping and iterating on production-level solutions.
Partner with Finance, Marketing, and Sales stakeholders to ensure AI deployments are scalable, secure, and measurably improve conversion rates.
Help establish ROI models and best practices for new AI-driven processes across the company.

We are looking for an ambitious, highly capable Full-Stack Software Engineer who leverages cutting-edge AI tools to multiply their productivity and own cloud features completely end-to-end, from initial design and architecture down to deployment, testing, and production support.

Were looking for cutting edge smart & capable engineers who can harness AI and can lead sophisticated projects from the requirement and design phases down to implementation, test and production delivery.

Key Responsibilities

End-to-End Feature Ownership: Own cloud features complete end-to-end, including backend services, frontend user interfaces, database schema design, automated testing, CI/CD deployment, and post-release support.
AI-Accelerated Development: Efficiently utilize advanced AI coding assistants and AI-driven workflows (e.g., Claude Code, Cursor, LLM-driven test generation) to design, code, debug, and deliver robust software at high velocity.
Frontend & Backend Engineering: Build and maintain scalable APIs and responsive, intuitive user interfaces (React, Go / Node.js).
Database & Infrastructure: Work with SQL/NoSQL databases and public cloud provider services to ensure high availability and optimal performance.
Quality & Automated Testing: Design, write, and execute comprehensive test suites (unit, integration, and E2E) to keep software stable and functional.
Production Support & Reliability: Participate in monitoring, troubleshooting, and supporting your delivered features in cloud environments.
Product & UX Collaboration: Work closely with Product and UX teams to rapidly turn concepts into seamless, user-friendly customer experiences.

Were looking for a Senior Backend Software Engineer to help develop our hyperscaling platform.
We have a lot of tasks on Golang and little less on Java and Python.

In this position, your responsibility will be to:
Developing fault-tolerant, reliable cloud services: cloud infrastructure, managed databases, managed Kubernetes, billing, monitoring systems, ML service and more.

We are building an inference & fine-tuning platform that makes every kind of foundation model - text, vision, audio, and emerging multimodal architectures - fast, reliable, and effortless to train & deploy at massive scale.

Some directions we currently working on and which you can be a part of:
Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency.

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation.
Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware

We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.



Some directions we are currently working on, and which you can be a part of:

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We're an SRE team within DevTools, looking for someone ready to help maintain and grow our systems
Your responsibilities will include:

Improving services based on user feedback

Building fault-tolerant, self-healing architecture

Finding ways to speed up our systems and reduce user friction

Modifying well-known closed- and open-source solutions Supporting our users

we are looking for a Senior Site Reliability Engineer (SRE)
Your responsibilities will include:

Ensure fault-tolerance, scale, and uninterrupted operations for the service.
Use cutting-edge cloud technology to solve a variety of infrastructure problems.
Implement and improve CI/CD processes.
We expect you to have:

Solid experience with programming languages (like Go, Python, or C++);
Solid understanding of classic algorithms and data structures;
Commercial experience with and deep understanding of Unix systems and network technology;
Experience with systems for containerization and configuration management (Ansible, Salt, Terraform, Docker, K8s, Helm).