Вакансии

What you will do Collaborate with UX designers, Product Management, and engineering teams to deliver robust security features alongside an optimal user experience. Work cross functionally with product squads and go-to-market teams to ensure smooth release of new features and products.

Обязанности: Отвечать за качество разработки ПО и покрытие функциональности тестами. Взаимодействовать с аналитиками, разработчиками и тестировщиками;

What You Will Do Test new features and run regression for iOS and Android apps; Test backend APIs and integrations: requests, responses, logs;

Location: Almaty, Kazakhstan Workplace: OnSite Software Engineer, Infrastructure Almaty, Kazakhstan Build the shared infrastructure that helps Higgsfield’s product teams ship quickly and run reliable services. You’ll own problems from finding the bottleneck to shipping a solution and supporting it in production. What you’ll do - Build shared services for queues, job scheduling, caching, configuration, and service communication. - Improve concurrency limits, resource allocation, and recovery for long-running media workloads. - Make database access, migrations, and connection management safer and faster. - Simplify service setup, testing, deployment, and production debugging. - Improve observability, investigate incidents, and reduce infrastructure cost per completed job. - Work directly with product engineers, ship focused improvements, and measure the results. Our release cycle is days, not quarters. Required - Experience running production systems on Kubernetes (K8s) and AWS, including deployments, scaling, networking, and troubleshooting. - Strong software engineering skills: you build shared services, tools, and automation that other engineers use. - Experience with databases, queues, and distributed systems, including performance debugging, safe rollouts, and recovery. - A difficult production problem you owned from an unclear starting point to a working solution. - Ability to set priorities, make practical tradeoffs, remove blockers, and ask for help when needed. - Clear communication, openness to feedback, and knowledge sharing so others can operate and extend your work. Nice to have - Cloudflare. - Infrastructure as code. - PostgreSQL, Redis, or event-streaming systems. - High-throughput media pipelines or AI workload orchestration. Interview process 1. Recruiter screen: expectations, logistics, and fit. 2. Technical interview: a difficult system you shipped, your decisions, and the results. 3. Additional technical interview, if needed. 4. Product fit interview: investigate a team’s bottleneck and plan a practical improvement, adoption, and safe rollout. 5. Offer. What we offer Competitive base pay in USD, equity, relocation support, and ownership of infrastructure used across our engineering teams.

Location: Almaty, Kazakhstan Workplace: OnSite Almaty, Kazakhstan · Trust & Safety Engineering Why this role exists Our ML and analytics teams already work on fraud detection and risk analysis. This role owns the backend that turns their signals into reliable protections across accounts, credits, payments, and generation. You'll integrate risk checks into product flows, build enforcement services and operator tools, and make sure controls work correctly in production. We're looking for an engineer we can trust to take a backend problem from an unclear starting point to a working solution. What you'll actually do - Risk integrations: connect existing ML scores and risk signals to account creation, credit usage, payments, and generation flows, with clear timeouts and fallback behavior. - Enforcement services: implement configurable rules, rate limits, account restrictions, and credit or payment controls with consistent behavior across services. - Operator tooling: build APIs and workflows for reviewing cases, inspecting decision history, and safely applying or reversing restrictions. - Production reliability: handle retries, duplicate events, concurrent requests, and safe rollouts; add monitoring and audit trails so failures can be traced and fixed. - Work in other teams’ codebases to add signals or interventions without hurting their stability or architecture. - Ownership: clarify the control with ML, analytics, and operations → scope the smallest useful implementation → ship → verify production behavior and review impact with the team. Our release cycle is days, not quarters, so your first shipped protection happens fast. Must-haves 1. You've taken a difficult problem from an unclear starting point to a working system used in production, and can explain your own decisions, contribution, and results. 2. You can build reliable backend services: APIs, data models, queues, transactions, idempotency, and concurrency. You can integrate external services and debug failures across the request and event lifecycle. 3. You choose the next steps, remove blockers, and ask for help when needed. You cut scope to test an idea, adapt from evidence, and keep working through difficult problems. 4. You work with ML, analytics, and operations to turn risk requirements into clear backend behavior. You use operator feedback and customer cases to improve controls and learn unfamiliar systems quickly. 5. You take feedback, change your mind when the evidence changes, and do the work the team needs. You share knowledge and responsibility so others can operate and extend what you build. Who this is not for - You prefer problems where requirements stay stable after launch. - You prefer work centered on training models or producing analysis rather than building and operating backend services. Nice to have - Fraud prevention, payments risk, or platform abuse experience. - Integrating risk-scoring services or building rules engines and rate limiters. - Case management, appeals, and risk operations tooling. - Familiarity with Stripe. - Experience building real-time scoring, graph-based account linkage systems. - Python/Go experience. Interview process 1. Recruiter screen: role expectations, logistics, and fit. 2. Technical interview: a difficult system you shipped, what you personally decided and built, and how your work made the team more capable without leaving it dependent on you. 3. Additional technical interview, if needed: a deeper discussion of technical skills and open questions. 4. Product fit interview: take an existing risk signal and an operator need, scope a backend protection, and work through failure cases and effects on legitimate customers. 5. Offer. What we offer Competitive base in USD, equity, relocation support, and ownership of protections that preserve both company revenue and customer trust.

Location: Almaty, Kazakhstan Workplace: OnSite Almaty, Kazakhstan · Trust & Safety Engineering Why this role exists An agent needs more than a model response to complete useful work. It needs tools, durable execution, permissions, context, and a way to recover when something fails. You'll build the backend that lets Higgsfield agents carry creative tasks from a user's request to a finished result. We're looking for an engineer we can trust with a problem: work out what is worth building, build it, and carry it through to users. What you'll actually do - Agent execution: build orchestration for multi-step tasks, tool calls, streaming updates, and cancellation. - Durable workflows: implement checkpoints, resumability, retries, and recovery without duplicating actions. - Tools and integrations: connect agents to generation, editing, and external services through clear, validated interfaces. - Execution boundaries: enforce user permissions, tenant isolation, resource limits, and approval requirements for consequential actions. - Evaluation and observability: trace failures, build regression cases, and improve task completion, latency, and cost. - Ownership: understand a real user task → define success and build evaluation cases → scope the simplest useful workflow → ship → measure completion, reliability, and dollar impact → improve from failed runs. Our release cycle is days, not quarters, so your first shipped agent capability happens fast. Must-haves 1. You've taken a difficult problem from an unclear starting point to a working system used in production, and can explain your own decisions, contribution, and results. 2. You can build reliable agent execution: state machines, concurrency, queues, recovery, model APIs, secure tool calls, and repeatable evaluations. You learn unfamiliar tools quickly and understand what you ship. 3. You choose the next steps, remove blockers, and ask for help when needed. You cut scope to test an idea, adapt from evidence, and keep working through difficult problems. 4. You investigate real user tasks and failed agent runs, use feedback to choose what to automate, and stop workflows that do not help users finish their work. 5. You take feedback, change your mind when the evidence changes, and do the work the team needs. You share knowledge and responsibility so others can operate and extend what you build. Who this is not for - You prefer deterministic application behavior without investigating model uncertainty. - You prefer prototyping without owning evaluation, recovery, and production reliability. Nice to have - Production agents, workflow engines, or developer tools. - High-throughput systems with heavy traffic, large data volumes, or many long-running jobs. - Agent context management, memory, subagents, or parallel tool calls. - Sandboxed execution and scoped authorization. - Multimodal workflows involving images, video, or audio. Interview process 1. Recruiter screen: role expectations, logistics, and fit. 2. Technical interview: a difficult system you shipped, what you personally decided and built, and how your work made the team more capable without leaving it dependent on you. 3. Additional technical interview, if needed: a deeper discussion of technical skills and open questions. 4. Product fit interview: scope a useful tool-using workflow, evaluate failures, and explain when user feedback would lead you to change or stop it. 5. Offer. What we offer Competitive base in USD, equity, relocation support, and ownership of systems that turn AI capabilities into completed creative work.

Location: Almaty, Kazakhstan Workplace: OnSite Almaty, Kazakhstan · Trust & Safety Engineering Why this role exists Higgsfield builds tools for AI creators: creative workflows, collaborative projects in Cinema Studio, and a community for sharing work. We need a backend engineer who combines deep database and architecture expertise with the initiative to own product outcomes, from the first idea to production. What you'll actually do - Build AI creation workflows, Cinema Studio collaboration, and community features: sharing, discovery, contests, profiles, and follows. - Own features and bugs through release and follow-up. Define API contracts and coordinate frontend work to deliver the complete feature. - Deliver responsive UX: live progress, timely updates, and clear failure recovery, even when it takes more engineering effort. - Remove database and service bottlenecks under high traffic; improve correctness, latency, and reliability. - Define success metrics, build dashboards and alerts, and track adoption and system health after launch. Must-haves 1. Deep database expertise: data modeling, indexes, query plans, partitioning, transactions, and locking. You diagnose production bottlenecks and understand the trade-offs behind your fixes. 2. Strong architecture judgment: service boundaries, APIs, asynchronous workflows, caching, and failure handling. Hands-on Kubernetes and CI/CD experience, from design and testing to deployment and debugging. 3. A track record of shipping and operating backend systems across multiple projects, including high-traffic workloads. You can explain your decisions and measurable results. 4. API security: authentication, object-level authorization, and tenant isolation. You prevent IDOR in reads and writes, including resource IDs in request bodies, and test access across users and workspaces. 5. You've built or studied social networks and collaborative tools, and can explain design trade-offs in feeds, follow graphs, notifications, sharing permissions, real-time sync, and concurrent edits. 6. High agency and ownership: you find worthwhile problems, propose improvements, remove blockers, and follow through without waiting for assignments. 7. Low ego: you welcome direct feedback, admit mistakes, change your mind with evidence, and help wherever the team needs you. 8. Ambition and optimism: hard goals and rapid growth energize you. You challenge assumptions constructively and commit to making ambitious ideas work. 9. Energy for an intense pace, shifting priorities, and urgent launches or production issues that can require work outside regular hours. Our tech stack - Languages and frameworks: Python (FastAPI), Go (Gin), and TypeScript (React/Next.js on the frontend). - Data: PostgreSQL and Redis; analytical databases such as BigQuery or ClickHouse. - Messaging: Kafka, Amazon SQS, and RabbitMQ; event-driven services and asynchronous workers. - AWS: S3, DynamoDB, ECR, and Secrets Manager. - Infrastructure: Docker, Kubernetes on Amazon EKS, Helm, Argo CD, and GitHub Actions. - Observability: Prometheus, Grafana, OpenTelemetry, and Sentry. Interview process 1. Recruiter screen: role expectations, logistics, and fit. 2. Technical interview: a difficult system you shipped, what you personally decided and built, and how your work made the team more capable without leaving it dependent on you. 3. Additional technical interview, if needed: a deeper discussion of technical skills and open questions. 4. Product fit interview: turn a user problem into a small first release, decide what to leave out, and explain how feedback would change the solution. 5. Offer. What we offer Competitive base in USD, equity, relocation support, and ownership of product experiences that directly shape how people create with AI.

Задача — обучение с нуля специализированных моделей на 1B–10B параметров. – Подготавливать данные и выстраивать пайплайны обучения.

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Задачи: — Анализ требований и приёмка технической документации — Создание чек-листов и тест-кейсов — Ручное тестирование ПО (браузерное, мобильное, SmartTV) — Оформление дефектов и проверка их исправления — Активное взаимодействие со смежными командами Требования: — Опыт тестирования клиент-серверных приложений от 1 года (web, mobile) — Опыт в тест-анализе и тест-дизайне — Знания основ тестирования и опыт применения знаний на практике — Опыт работы с системой контроля версий GIT на уровне Reporter — Опыт работы с багтрекинговыми системами — Опыт работы с системами управления тестирования Условия: — Формат работы: гибрид, от 1 до 3 дней из офиса — Зарплата: 120 000 рублей на руки — Оформление по ТК РФ

Обязанности анализ бизнес-требований и системных требований анализ кода работы с данными (PL/SQL) (по необходимости) участие в выпуске релизов (тестирование, определение состава релизов, участие в приемо-сдаточных испытаниях) работы по поиску и воспроизведению дефектов, выявленных в процессе эксплуатации написание авто-тестов погружение в DevOps задачи.

-Feature development: You will be owning features on our web platforms – currently the Phantom Extension and our website. -New initiatives and products: You will be leading initiatives to add support for other blockchains to Phantom, and to launch new complementary products or infrastructure.

• Взаимодействие с разработчиками, DevOps, архитекторами и QA для тюнинга производительности.

• Разработка, поддержка и выполнение ручных и автоматизированных планов тестирования для мобильных, веб- и серверных систем. • Валидация потоков финансовых транзакций, выплат...

• Разрабатывать ядро прокси, управлять соединениями и жизненным циклом сессий. • Оптимизировать latency, throughput и потребление памяти, устранять блокировки и паузы GC.

-разработка и оптимизация ETL/ELT-конвейеров для обработки больших данных. -настройка и администрирование Hadoop/Spark-инфраструктуры (HDFS, YARN, Hive, HBase).

-Первые месяцы - работа руками наравне с командой, дальше - перевод в процессы и автоматизацию. -убрать SPOF в проде.

— Поддержка и развитие AI-сервиса на Python и LangChain. — Интеграция LLM-моделей, разработка AI-агентов и RAG-систем.