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Node.js

Kubernetes

Python

PyTorch

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<h3>Role</h3>We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users. <h3>Focus</h3><ul><li>Build end-to-end product features across frontend, backend, and AI integrations</li><li>Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.</li><li>Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions</li><li>Design real-time AI interactions with streaming, partial results, and tight latency constraints</li><li>Improve system reliability, observability, and fallback mechanisms</li><li>Collaborate closely with ML, backend, and product teams to ship features end-to-end</li><li>Continuously iterate based on real usage and failure modes</li></ul> <h3>Ideal Experiences</h3><ul><li>Strong experience in full stack engineering (frontend + backend)</li><li>Solid understanding of system design and API architecture</li><li>Experience working with LLMs, RAG systems, or AI-powered applications</li><li>Ability to handle ambiguity and make pragmatic engineering decisions</li><li>Strong ownership - able to take features from idea to production</li><li>Comfort working in fast-moving environments with evolving requirements</li></ul>

Full Stack Engineer, AI systems

AI/ML

Fullstack

Node.js developer

Python developer

Machine Learning

JavaScript

Typescript

As an Android Software Engineer, you own the Android client experience, how AI feels, behaves, and performs on mobile devices. This is not a thin client role. You will build a production Android application where AI interactions are core to the product, and performance, reliability, and clarity matter. <h3>Focus</h3><ul><li>Build and maintain production Android apps using Kotlin.</li><li>Integrate AI-powered features (chat, vision, voice, recommendations) via backend APIs.</li><li>Design UX patterns for AI interactions, including streaming responses, retries, and partial results.</li><li>Optimize performance, memory usage, and responsiveness for AI-heavy flows.</li><li>Implement analytics, logging, and feedback capture to support AI evaluation and iteration.</li><li>Collaborate closely with backend and ML engineers on API contracts and system behavior.</li><li>Ensure app stability, security, and scalability in production environments.</li></ul> <h3>Ideal Experiences</h3><ul><li>3+ years of Android development experience using Kotlin.</li><li>Hands-on experience integrating AI features (e.g. LLM, vision, speech APIs).</li><li>Strong understanding of asynchronous programming (Coroutines, Flow).</li><li>Familiarity with REST or gRPC APIs and structured data formats.</li><li>Strong debugging and performance profiling skills.</li><li>Comfort building in environments with latency, partial failure, and non-deterministic behavior.</li><li>Experience with MLKit or light on-device inference.</li><li>Published production apps on the Google Play Store.</li></ul>

Android Software Engineer

Mobile

Android

Kotlin developer

Kotlin

About the RoleAs an ML Platform Engineer, you will build the infrastructure and systems that power A1's AI capabilities.You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement.You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.Focus<ul><li>Build and operate the ML infrastructure and platforms powering A1’s AI products</li><li>Design systems for model training, evaluation, deployment, inference, and experimentation</li><li>Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads</li><li>Improve reliability, scalability, latency, and cost efficiency of AI systems</li><li>Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement</li><li>Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster</li><li>Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions</li><li>Build production observability, monitoring, tracing, and alerting for AI/ML workloads</li><li>Improve AI systems across reliability, scalability, latency, throughput, and cost</li><li>Identify bottlenecks across the ML stack and continuously improve system performance</li><li>Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure</li></ul>

Machine Learning Platform Engineer

<h3>Role</h3>As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage. <h3>Focus</h3><ul><li>Build and ship AI features end-to-end (model → system → user experience)</li><li>Design and iterate on prompts, tools, memory, and agent workflows</li><li>Turn raw model outputs into structured, reliable, and predictable behaviors</li><li>Debug issues across the full stack (model, orchestration, infra, UX)</li><li>Optimize for latency, cost, and production reliability</li><li>Develop lightweight evaluation frameworks to measure real-world performance</li><li>Work closely with product and engineering to translate ambiguous problems into working systems</li></ul>

Applied AI Engineer

<h3>Role</h3>As an iOS Software Engineer, you own the iOS client experience, how AI feels, responds, and performs in users’ hands. This is not a thin integration role. You will build a production-grade iOS application where AI interactions are central to product experience, and performance, reliability, and user trust are critical. <h3>Focus</h3><ul><li>Build and maintain iOS applications using Swift and SwiftUI.</li><li>Integrate AI-powered features through backend APIs.</li><li>Design UX patterns for AI interactions, including loading states, streaming responses, retries, and fallbacks.</li><li>Optimize performance, memory usage, and battery efficiency.</li><li>Capture user signals and feedback to support AI evaluation and iteration.</li><li>Collaborate closely with backend and ML engineers on API design and system behavior.</li><li>Maintain high App Store quality, stability, and production reliability.</li></ul> <h3>Ideal Experiences</h3><ul><li>3+ years of iOS development experience using Swift.</li><li>Hands-on experience integrating AI-powered features into mobile apps.</li><li>Strong understanding of async/await, concurrency, and background tasks.</li><li>Solid iOS performance and memory optimization skills.</li><li>Experience shipping and maintaining production iOS apps.</li><li>Comfort designing for latency, partial failure, and non-deterministic AI behavior.</li><li>Exposure to CoreML or light on-device ML.</li><li>Familiarity with feature flags or remote configuration systems.</li><li>Strong intuition for AI UX patterns and user trust considerations.</li></ul>

iOS Software Engineer

Swift developer

Swift

<h3>Role</h3>As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. Your work sits between models and users, where latency, correctness, reliability, and cost directly impact real-world experience.You will build and operate production systems that turn model capability into fast, stable, observable APIs used across mobile and desktop clients. <h3>Focus</h3><ul><li>Build and operate backend systems that serve AI-powered features in production.</li><li>Design inference pipelines, orchestration layers, and service boundaries around models.</li><li>Own production concerns: monitoring, logging, alerting, and incident response.</li><li>Optimize latency and throughput across inference, caching, batching, and streaming.</li></ul> <h3>Ideal Experiences</h3><ul><li>Strong backend engineering fundamentals in production environments.</li><li>Experience running high-throughput, low-latency services.</li><li>Familiarity with AI inference patterns (LLMs, embeddings, multimodal).</li><li>Comfortable debugging distributed systems under load.</li><li>Bias toward shipping and learning from production behavior.</li></ul>

Backend Engineer, AI (Agent Systems)

Backend

About the RoleAs an LLM Application Engineer, you will build the intelligence layer that powers A1's AI experiences.You will work at the intersection of LLMs, software engineering, and product - designing agent workflows, improving model behaviour, and turning AI capabilities into reliable user experiences.You will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation system and continuously improving AI behaviour in production.Focus<ul><li>Build and ship LLM-powered applications and AI agent workflows</li><li>Design systems for reasoning, planning, memory, tool uuse and multi-step execution</li><li>Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions</li><li>Integrate LLMs with APIs, databases, search, internal services, and external tools.</li><li>Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour</li><li>Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions</li><li>Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX</li><li>Optimise AI systems for quality, latency, and cost</li><li>Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions</li><li>Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement</li></ul>

LLM Application Engineer

<h3>Role</h3>As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage. <h3>Focus</h3><ul><li>Build and ship AI features end-to-end (model → system → user experience)</li><li>Design and iterate on prompts, tools, memory, and agent workflows</li><li>Turn raw model outputs into structured, reliable, and predictable behaviors</li><li>Debug issues across the full stack (model, orchestration, infra, UX)</li><li>Optimize for latency, cost, and production reliability</li><li>Develop lightweight evaluation frameworks to measure real-world performance</li><li>Work closely with product and engineering to translate ambiguous problems into working systems</li></ul>

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