Вакансии

Задачи : -Примерно половина времени будет посвящена разработке, вторая половина — поддержанию скорости, безопасности и качества всей системы. -Создавать функции целиком: схема данных, API, фоновые задачи, интерфейс и запуск в продакшене.

Location: Romania; Serbia; Poland Job Overview The Technical Project Manager will be responsible for end-to-end delivery of technology projects , working closely with Engineering, Product, DevOps, Infrastructure, QA, Security and business stakeholders. The ideal candidate should have strong technical understanding and be able to challenge estimates, identify dependencies, manage risks, drive engineering teams and communicate effectively with senior stakeholders. Key Responsibilities Project & Delivery Management Own end-to-end delivery across multiple technical projects and workstreams. Define project scope, milestones, deliverables, timelines and success criteria. Create and maintain detailed project plans, delivery roadmaps and release plans. Track progress against commitments and proactively manage delays. Drive projects from requirements through development, UAT, deployment and production stabilization. Ensure clear ownership and accountability across teams. Technical Project Management Work closely with Engineering, Architecture, DevOps, Infrastructure, QA and Security teams. Understand technical requirements, architecture, APIs, integrations, databases, cloud infrastructure and deployment processes. Review estimates and challenge assumptions where required. Identify technical dependencies, risks and resource constraints early. Coordinate resolution of technical blockers and cross-team dependencies. Ensure technical decisions and requirements are properly documented. Agile & Governance Drive Sprint Planning, backlog refinement, daily tracking, sprint reviews and retrospectives. Maintain project dashboards and delivery metrics. Ensure accurate capacity planning and resource allocation. Establish effective governance without creating unnecessary process overhead. Maintain RAID logs, action trackers and decision logs. Stakeholder Management Act as the primary delivery interface between engineering teams, product/business stakeholders and leadership. Provide clear and fact-based project status reporting. Escalate risks and issues early with recommended actions. Manage expectations around scope, timelines, resources and priorities. Conduct regular stakeholder reviews and project governance meetings. Resource Management Plan resources based on actual capacity and project requirements. Identify resource gaps and provide data-backed recommendations. Optimize utilization of existing engineering resources before requesting additional headcount. Coordinate cross-functional teams across India and international locations. Quality, UAT & Release Management Establish UAT plans, acceptance criteria and release readiness. Track defects and ensure timely resolution of critical issues. Coordinate release and deployment activities with DevOps/Infrastructure teams. Ensure production readiness and post-release stabilization. Technical Exposure Candidates should have working knowledge of several of the following: Web applications and SaaS platforms Frontend / Backend architecture APIs and third-party integrations Microservices Databases and data platforms Cloud platforms — AWS / Azure / GCP DevOps and CI/CD Docker / Kubernetes Identity & Access Management Security and authentication AI/ML platforms and infrastructure Monitoring, logging and observability Performance and scalability QA automation and testing frameworks Required Skills 8–12+ years of experience in technology/project/program delivery. Strong experience managing software engineering projects. Experience managing Frontend, Backend, QA, DevOps and Infrastructure teams . Strong understanding of Agile/Scrum methodologies. Proven experience managing multiple concurrent projects. Strong planning, estimation and dependency-management skills. Excellent stakeholder and communication skills. Experience working with distributed/global teams. Strong problem-solving and escalation-management capability. Experience with Jira, Tempo, Confluence or similar project management tools. Ability to manage delivery based on capacity, priorities and measurable outcomes . Preferred PMP / PRINCE2 / Scrum certification. Experience with SaaS / B2B technology platforms. Experience in Ecommerce, cloud, AI, data projects. Experience managing teams of 20+ engineers across multiple disciplines .

Location: Gurgaon, India Job Overview The Technical Project Manager will be responsible for end-to-end delivery of technology projects , working closely with Engineering, Product, DevOps, Infrastructure, QA, Security and business stakeholders. The ideal candidate should have strong technical understanding and be able to challenge estimates, identify dependencies, manage risks, drive engineering teams and communicate effectively with senior stakeholders. Key Responsibilities Project & Delivery Management Own end-to-end delivery across multiple technical projects and workstreams. Define project scope, milestones, deliverables, timelines and success criteria. Create and maintain detailed project plans, delivery roadmaps and release plans. Track progress against commitments and proactively manage delays. Drive projects from requirements through development, UAT, deployment and production stabilization. Ensure clear ownership and accountability across teams. Technical Project Management Work closely with Engineering, Architecture, DevOps, Infrastructure, QA and Security teams. Understand technical requirements, architecture, APIs, integrations, databases, cloud infrastructure and deployment processes. Review estimates and challenge assumptions where required. Identify technical dependencies, risks and resource constraints early. Coordinate resolution of technical blockers and cross-team dependencies. Ensure technical decisions and requirements are properly documented. Agile & Governance Drive Sprint Planning, backlog refinement, daily tracking, sprint reviews and retrospectives. Maintain project dashboards and delivery metrics. Ensure accurate capacity planning and resource allocation. Establish effective governance without creating unnecessary process overhead. Maintain RAID logs, action trackers and decision logs. Stakeholder Management Act as the primary delivery interface between engineering teams, product/business stakeholders and leadership. Provide clear and fact-based project status reporting. Escalate risks and issues early with recommended actions. Manage expectations around scope, timelines, resources and priorities. Conduct regular stakeholder reviews and project governance meetings. Resource Management Plan resources based on actual capacity and project requirements. Identify resource gaps and provide data-backed recommendations. Optimize utilization of existing engineering resources before requesting additional headcount. Coordinate cross-functional teams across India and international locations. Quality, UAT & Release Management Establish UAT plans, acceptance criteria and release readiness. Track defects and ensure timely resolution of critical issues. Coordinate release and deployment activities with DevOps/Infrastructure teams. Ensure production readiness and post-release stabilization. Technical Exposure Candidates should have working knowledge of several of the following: Web applications and SaaS platforms Frontend / Backend architecture APIs and third-party integrations Microservices Databases and data platforms Cloud platforms — AWS / Azure / GCP DevOps and CI/CD Docker / Kubernetes Identity & Access Management Security and authentication AI/ML platforms and infrastructure Monitoring, logging and observability Performance and scalability QA automation and testing frameworks Required Skills 8–12+ years of experience in technology/project/program delivery. Strong experience managing software engineering projects. Experience managing Frontend, Backend, QA, DevOps and Infrastructure teams . Strong understanding of Agile/Scrum methodologies. Proven experience managing multiple concurrent projects. Strong planning, estimation and dependency-management skills. Excellent stakeholder and communication skills. Experience working with distributed/global teams. Strong problem-solving and escalation-management capability. Experience with Jira, Tempo, Confluence or similar project management tools. Ability to manage delivery based on capacity, priorities and measurable outcomes . Preferred PMP / PRINCE2 / Scrum certification. Experience with SaaS / B2B technology platforms. Experience in Ecommerce, cloud, AI, data projects. Experience managing teams of 20+ engineers across multiple disciplines .

Публичное API: Развивать OpenAI/Anthropic-совместимое API, API медиамоделей и транскрибации. Отвечать за стабильность, идемпотентность и мониторинг.

Вам предстоит проектировать и разрабатывать алгоритмы и сервисы для обработки разнородных данных в распределенной системе.

проектировать и улучшать релизные процессы: строить высоконагруженные пайплайны сборки и развертывания проектов в средах Kubernetes/OpenShift с использованием Jenkins и ArgoCD. разрабатывать и поддерживать внутренние DevOps-инструменты и автоматизации на базе Java/Groovy, интегрируя их с экосистемой платформы.

Принять инфраструктуру от подрядчика: инвентаризация сред и компонентов, документирование, наведение порядка в доступах и секретах. Развернуть наблюдаемость: метрики, логи, трассировка, осмысленные оповещения вместо шума.

Location: Canada; United States Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ THE ROLE Docker Hardened Images (DHI) is Docker's catalogue of security-hardened, enterprise-grade container images and Helm charts, built to be minimal, up-to-date, and safe to deploy in regulated and security-conscious environments. We're looking for a Principal Software Engineer to set the long-term technical direction for this catalogue and shape how the broader container security ecosystem evolves inside and beyond Docker. At this level, you'll work across organizations, not just within one team. You'll set direction for major domains, resolve the highest-ambiguity problems in container supply chain security, and grow your impact through architectural leadership, cross-functional alignment, and influence on industry standards. This is a pure individual contributor role; influence comes through technical vision, design, and the ability to move organizations. _______________________________________________________________________ RESPONSIBILITIES - Own the architecture for DHI as a platform, making high-leverage decisions that define how images and Helm charts are authored, secured, distributed, and evolved across the entire catalogue and across Docker's broader product surface. - Define and drive multi-quarter technical roadmaps for the container hardening domain, balancing upstream velocity, supply chain security posture, customer compliance requirements, and long-term catalogue scalability. - Identify systemic patterns from customer, operational, and upstream signals and translate them into architectural changes that span the catalogue, not just individual images or charts. - Build alignment across teams and executives on technical strategy, tradeoffs, and sequencing, including product, security, infrastructure, and customer-facing functions. - Represent Docker as a trusted technical voice in escalations, key enterprise customer engagements, and upstream OSS communities on issues that shape the future of secure container delivery. - Set conventions and standards at scale, defining the patterns others build within for image definition, Helm chart adaptation, hardening strategy, and supply chain tooling (Sigstore, SBOM, SLSA), and evolving those standards as the ecosystem changes. - Drive the hardest, most ambiguous packaging problems where the right answer requires reasoning across upstream dynamics, security tradeoffs, multi-arch constraints, and customer impact simultaneously. - Shape the integration test infrastructure and developer tooling strategy so that DHI's engineering platform scales with the catalogue and raises quality across the team. - Mentor and raise the technical ceiling for staff and senior engineers across the org, through review, design, and well-chosen architectural decisions that create durable leverage. - Take part in the paid on-call rotation; respond to incidents, debug production issues, and drive systemic improvements in reliability. _______________________________________________________________________ QUALIFICATIONS - 10+ years of backend engineering experience, including extensive work on production-grade, distributed systems at scale. - Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience. - Ability to set multi-quarter technical roadmaps and align stakeholders (engineering, product, and executives) on strategy and tradeoffs. - Deep expertise in the container and Kubernetes ecosystem: you have strong, grounded opinions about cert-manager, kyverno, grafana, istio, and similar projects, and you can reason about tradeoffs at the ecosystem level, not just the image level. - Mastery of container supply chain security concepts (provenance, attestation, SBOM, signing, SLSA) and experience driving posture decisions across an organization rather than implementing them on a single project. - Strong software engineering fundamentals: code review, testing, source control, CI/CD, and Go sufficient to shape infrastructure and harness design. - Track record of technical influence without authority across multiple teams or organizations, raising quality through design docs, standards, review, and mentorship. - Experience navigating upstream OSS communities as a decision-maker, representing a downstream organization's requirements and shaping upstream direction on security-relevant issues. - Comfort working across remote, distributed teams and communicating complex technical strategy clearly to both technical and non-technical audiences. BONUS BUT NOT REQUIRED - Experience as a package maintainer at a Linux distribution, Homebrew, or comparable ecosystem. - Hands-on experience implementing or operationalizing supply chain tooling (Sigstore, SBOM, SLSA) at org scale. - Experience in regulated environments (FedRAMP, FIPS, PCI) with direct exposure to compliance requirements shaping engineering decisions. - Prior Principal or Distinguished IC experience on a platform, security, or developer-tools team. - Experience engaging directly with enterprise customers on container security architecture. _______________________________________________________________________ WHAT TO EXPECT FIRST 30 DAYS - Align with leadership on the most critical org-wide technical risks and opportunities in container supply chain security. - Develop a point of view on DHI's current architectural constraints and where the highest-leverage interventions are. - Begin mapping the cross-functional landscape: product priorities, upstream pressures, customer compliance signals, and engineering gaps. FIRST 90 DAYS - Drive an architectural decision that unblocks multiple teams and reduces systemic risk across the catalogue. - Establish a feedback loop from customer, operational, and upstream signals into the multi-quarter roadmap. - Engage upstream OSS communities on at least one DHI-relevant issue with meaningful influence on direction. FIRST YEAR - Deliver a major platform or standards evolution with broad adoption across DHI and adjacent teams. - Create durable alignment across engineering, product, and security on catalogue architecture, supply chain posture, and hardening strategy. - Raise the technical ceiling for the engineers around you, measurably improving review quality, architectural consistency, and the team's ability to operate independently at a higher level. Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs. _______________________________________________________________________ COMPENSATION & EQUITY All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location. Canada: CA$245,913 – CA$397,139 base United States: $180,500 – $291,500 base ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Location: New York ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. The Role We are seeking highly experienced Site Reliability Engineers (SRE) to shape the reliability, scalability and performance of our platform and customer facing applications. You will work closely with our software engineers and research teams to ensure our systems meet and exceed our internal and external customers' expectations.     What You Will Do As a Site Reliability Engineer, you balance the day-to-day operations on production systems with long-term software engineering improvements to reduce operational toil and foster the reliability, availability, and performance of these systems.   Operations • Design, build, and maintain scalable, highly available and fault-tolerant infrastructures to support our web services and ML workloads • Make sure our platform, inference and model training environments are always highly available and enable seamless replication of work environments across several HPC clusters • Operate systems and troubleshoot issues in production environments (interrupts, on-call responses, users admin, data extraction, infrastructure scaling, etc.) • Implement and improve monitoring, alerting, and incident response systems to ensure optimal system performance and minimize downtime • Implement and maintain workflows and tools (CI/CD, containerization, orchestration, monitoring, logging and alerting systems) for both our client-facing APIs and large training runs • Participate occasionally in on-call rotations to respond to incidents and perform root cause analysis to prevent future occurrences   Development • Drive continuous improvement in infrastructure automation, deployment, and orchestration using tools like Kubernetes, Flux, Terraform • Collaborate with AI/ML researchers to develop and implement solutions that enable safe and reproducible model-training experiments • Build a cloud-agnostic platform offering an abstraction layer between science and infrastructure • Design and develop new workflows and tooling to improve to the reliability, availability and performance of our systems (automation scripts, refactoring, new API-based features, web apps, dashboards, etc.) • Collaborate with the security team to ensure infrastructure adheres to best security practices and compliance requirements • Document processes and procedures to ensure consistency and knowledge sharing across the team • Contribute to open-source projects, research publications, blog articles and conferences   What We're Looking For • Master’s degree in Computer Science, Engineering or a related field • 7+ years of experience in a DevOps/SRE role • Strong experience with cloud computing and highly available distributed systems • Exposure to site reliability issues in critical environments (issue root cause analysis, in-production troubleshooting, on-call rotations...) • Experience working against reliability KPIs (observability, alerting, SLAs) • Hands-on experience with CI/CD, containerization and orchestration tools (Docker, Kubernetes...) • Knowledge of monitoring, logging, alerting and observability tools (Prometheus, Grafana, ELK Stack, Datadog...) • Familiarity with infrastructure-as-code tools like Terraform or CloudFormation • Proficiency in scripting languages (Python, Go, Bash...) and knowledge of software development best practices • Strong understanding of networking, security, and system administration concepts • Excellent problem-solving and communication skills • Self-motivated and able to work well in a fast-paced startup environment   Your application will be all the more interesting if you also have: • experience in an AI/ML environment • experience of high-performance computing (HPC) systems and workload managers (Slurm) • worked with modern AI-oriented solutions (Fluidstack, Coreweave, Vast...) WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Remote US At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. Affirm's engineering team is building a large-scale, fault-tolerant, and global infrastructure that is shared across multiple financial products, merchants, and vendors. Ensuring that our infrastructure is transparently available to all engineers is critical to the success of the business. We pride ourselves on our culture across engineering of writing blameless postmortems as well as writing detailed tech specs and gathering feedback before large changes to systems. The AI Platform team owns Affirm's paved path for building, running, evaluating, and governing AI systems. Our customers are not only Affirm users, but also every engineer and a growing set of production workloads at Affirm. As a team, we are responsible for building and exposing simple, governed interfaces to models, tools, and agents, with identity, cost attribution, reliability, and security built in. Examples of components we own include the LLM Gateway that fronts every approved model (Claude, ChatGPT, and open-weight models across AWS Bedrock and other providers), the MCP Gateway that mediates and audits tool calls, the Agent Runtime that gives agentic workloads bounded execution, and the evals and observability layer that tells teams whether an AI change is better, cheaper, or safe to ship. We are early: the gateway is in daily use across engineering, the first production agent is live, and the next year takes us from developer productivity to production inference and agents. What You'll Do Plan, execute, and own technical decisions and projects within the AI Platform team, from design through launch and on-call. Design and build the gateway, runtime, and evaluation services that take AI workloads at Affirm from pilot to production, securely scaling across millions of users and their transactions. Make AI spend attributable and governable: build the identity, quota, routing, caching, and failover controls that let teams adopt new models and tools without re-solving the same platform problems. Partner with security, infrastructure, and product engineering teams to set the guardrails for agentic workloads and the on-behalf-of identity model for tool access. Create and monitor metrics for your team's systems, and own the operational health of what you ship: monitoring, alerting, runbooks, and on-call. Set and improve code review and design standards, and advocate for them beyond your team through writing and tech talks. Use AI tools and agentic workflows every day as a multiplier for your own code, design review, and operations, and push the team on what a small AI-native team can deliver. Educate, mentor, and grow engineers in the team, and drive best practices around design, coding, and testing. What We Look For You have 4+ years of experience designing, developing and launching backend systems at scale using languages like Python or Kotlin. You have a track record of developing highly available distributed systems using technologies like AWS, MySQL and Kubernetes. You have practical experience building with LLMs, agents, or AI frameworks in production: inference APIs, LLM gateways (LiteLLM or similar), MCP, tool calling, evals, or agent runtimes. You understand AI usage and the infrastructure needed to support and leverage AI workloads across different disciplines. You have experience defining a technical plan for the delivery of a significant feature or system component with an elegant, simple and extensible design. You write high quality code that is easily understood and used by others. You are proficient at making significant changes in a large code base, and have developed a suite of tools and practices that enable you and your team to do so safely. Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders. You have strong verbal and written communication skills that support effective collaboration with our global engineering team. This position requires either equivalent practical experience or a Bachelor’s degree in a related field. Pay Grade - N Equity Grade - 9 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.) USA base pay range (CA, WA, NY, NJ, CT) per year: $ 195,000 - 255,000 USA base pay range (all other U.S. states) per year: $173,000-233,000 #LI-Remote Remote-first with flexibility built in Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience. Benefits designed for you Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights: Health coverage at no cost: We cover 100% of premiums for employees and their dependents. Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you. Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to. Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount. We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help. For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law. By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and consent to the use of your personal information as described.

Location: United States (Remote) Workplace: Remote ROLE SUMMARY We’re looking for a Software Engineer II to join the Cloud Applications team. This team builds and operates critical parts of the Temporal Cloud customer experience, including Namespaces https://docs.temporal.io/cloud/namespaces, Audit Logs https://docs.temporal.io/cloud/audit-logs, and Export https://docs.temporal.io/cloud/export. Namespaces are the core unit of isolation within Temporal Cloud, providing security boundaries, workflow management, unique identifiers, and dedicated gRPC endpoints. Audit Logs and Export give customers reliable access to the operational and compliance data they depend on. We're looking for an engineer who is excited to learn these systems and contribute to their scalability, reliability, and developer experience. This is a hands-on role where you'll collaborate with experienced engineers, product managers and partner teams to design, build, and operate backend services and data pipelines while growing your distributed-systems expertise. WHAT YOU'LL DO - Design, implement, test, and ship well-scoped features and improvements for Temporal Cloud with support from teammates. - Build and improve services that support namespace provisioning, configuration, scalability, and reliability. - Develop data pipelines that process high-volume usage and operational data across multiple tenants. - Make thoughtful engineering tradeoffs with guidance, balancing data freshness, query cost, tenant isolation, reliability, and operational complexity. - Improve service operability through tests, instrumentation, dashboards, alerts, runbooks, and documentation. - Participate in code reviews, design discussions, incident response, and production debugging. - Collaborate across engineering, product, and customer-facing teams to understand requirements and deliver scoped projects safely. - Improve developer and customer experience through automation, better tooling, and incremental product improvements. WHAT YOU'LL BRING - Experience building and operating production backend systems in a cloud environment. - Proficiency in Go or another applicable backend or systems language, with the ability to ramp up in an existing codebase. - Strong computer science fundamentals, including data structures, concurrency basics, testing, and debugging. - Working knowledge of distributed-systems concepts such as reliability, fault tolerance, consistency, scaling, and failure handling. - Experience with databases, data pipelines, messaging systems, or object storage. - The ability to take a scoped problem, break it into smaller tasks, implement a solution end to end, and incorporate feedback. - An operational mindset, including familiarity with logging, metrics, tracing, deployment, and production troubleshooting. - Comfort collaborating across teams and communicating progress, risks, and technical tradeoffs clearly. - A commitment to correctness, maintainability, and leaving systems better than you found them. NICE TO HAVE - 3+ years of work experience - Experience with Go. - Experience with PostgreSQL, Redshift, Kinesis, Firehose - Experience with S3, Google Cloud Storage, Azure Blob Storage, or other object-storage systems. - Familiarity with Kubernetes and cloud infrastructure. - Experience supporting on-call rotations, investigating incidents, or improving production observability. - Familiarity with Temporal concepts—or a strong interest in learning the platform. TECHNOLOGY STACK - Primary language: Go - Data storage: PostgreSQL, Redshift, S3,Google Cloud Storage, Azure Blob Storage, ClickHouse - Data processing: Kinesis, Firehose - Infrastructure: Kubernetes - Core framework: Temporal You do not need experience with every technology in our stack. We value strong engineering fundamentals and the ability to learn unfamiliar systems. Temporal Technologies is an Equal Opportunity Employer. Temporal Technologies does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need. We embrace and celebrate differences and diversity. Temporal is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. If you need to request a reasonable accommodation, please let your Recruiter know so we can assist.

Location: United States (Remote) Workplace: Remote About Teleport Don't wait for the future of infrastructure. Be part of it. Teleport is the AI Infrastructure Identity Company. We're solving one of the hardest problems in security: giving every human, machine, workload, and AI agent a cryptographically secured identity, improving engineering velocity while maintaining security. We make trusted computing simple. This gives you the freedom, power, and autonomy to build and innovate with confidence. Remote-first and globally distributed, we work with companies like Nasdaq, DoorDash, and Elastic to secure infrastructure for an AI world. As a member of the Teleport Customer Solutions Team, you will own the full post-sales lifecycle for a portfolio of strategic customers: implementation, technical support, ongoing business reviews, and identifying expansion opportunities. You will be responsible for implementing, supporting, evangelizing, and expanding Teleport at our top customers, and dedicated to proactively driving adoption so that they achieve the maximum value from all Teleport products and solutions. This is a deeply technical role. Many of our customers run Teleport on Kubernetes, and you will be expected to operate comfortably at the cluster level: designing and deploying Teleport in customer environments, troubleshooting failing workloads, and debugging access, networking, and certificate issues alongside customer engineers. You will work directly with customers to design solution architecture, deploy and configure Teleport hands-on, share implementation best practices, triage and debug technical issues, develop and maintain training programs, evangelize the solution to a wider customer organization, and plan and assist with enterprise-wide rollouts. You will act as a customer champion within Teleport and help prioritize product feedback by working with the Product team. As a Senior Technical Account Manager, you will be the primary lead for identifying and providing initial triage of technical blockers via shared chat, ticket, and meetings. As our company grows and we launch new offerings, the requirements of this position may shift. You should be comfortable with change and have an interest in continually learning and advancing your skillset. What you’ll bring - Deep, hands-on Kubernetes experience. You have built clusters with kubeadm (not just managed services), configured RBAC and certificate-based user access, and debugged failing workloads, networking, and control-plane issues. CKA certification is strongly preferred; CKAD is a plus. - Experience with cert-manager and TLS certificate management, Helm, and GitOps tooling (ArgoCD or Flux) - Demonstrable experience with the following security principles and technologies: SSH, PKI, 2FA, SSO (SAML, OIDC), and RBAC - Linux administration skills - Hands-on experience with one of the major public cloud providers: AWS, GCP, or Azure (certifications are a plus) - 8+ years of experience in customer-facing software onboarding, software services, solution architecting, or system administration roles - Exceptional hands-on troubleshooting, testing, and debugging skills, with the tenacity to identify and resolve technical issues as they arise - Outstanding soft skills and excellent verbal and written communication - Project management experience and the ability to handle multiple clients simultaneously What you’ll be doing - Design solution architecture for Teleport in customer environments - Deploy and configure Teleport in customer environments, including Kubernetes-based deployments, and communicate implementation best practices to the customer - Act as the point person for technical issues, troubleshooting Kubernetes-based customer environments (cluster, networking, certificate, and access issues) and escalating high-complexity cases to Engineering - Work closely with Sales and Product to identify customer needs tied to tangible upsell and cross-sell opportunities - Plan and deliver Quarterly Business Reviews for customers, drive adoption within the customer organization, and plan and assist with enterprise-wide rollouts of Teleport - Develop, maintain, and deliver customized training, and evangelize the solution to a wider customer organization - Maintain strong working relationships with key client stakeholders and expand those relationships into other parts of the organization - Document each customer’s business goals and challenges to align Teleport with the most value possible - Serve as a subject matter expert on existing and upcoming Teleport solutions for customers and teammates, staying up to date on the latest releases - Collaborate with Sales and Product to provide valuable feedback for product improvement - Support our broader customer base seeking new solutions and best practice recommendations Our interview process Our interview process includes a hands-on technical assessment. You will build a kubeadm Kubernetes cluster, configure RBAC with certificate-based user access, deploy an application, present your design and its tradeoffs, and work through live Kubernetes troubleshooting scenarios. Candidates should be comfortable operating Kubernetes at this level without relying on managed-service abstractions. You are welcome to use AI tools for the take-home portion, with full disclosure of how they were used. Why Teleport You're joining a company where the problem is real, the team is small, and your work shows up directly in the product. We're not a big company, you won't get lost in a crowd. You'll have the freedom and autonomy to do what you're great at, alongside teammates who care about doing it right and want to see you succeed. The work is collaborative, there's real room to grow, and the mission is one worth showing up for. Remote-first and globally distributed, we're genuinely passionate about what we're building. The Benefits At Teleport, we believe your career is more important than a list of perks. That's why we focus on making your day-to-day the best it can be — giving you the autonomy, access, and support to do the best work of your career. - Extensive health coverage - Annual expense budget - Rest and recovery policies that maximize your ability to recharge - Investment in your future with retirement savings plans - Professional development opportunities Teleport is an equal opportunity employer and does not discriminate against any employee or applicant on the basis of age, color, disability, gender, national origin, race, religion, sexual orientation, veteran status, or any classifications protected by federal, state, or local law. Candidate Privacy Notice: For information about our collection and processing of job applicant personal data for this position, please see our Job Applicant Privacy Policy and Notice of Collection at goteleport.com/legal/apply/job-applicant/ http://goteleport.com/legal/apply/job-applicant/

Location: New York, New York, United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Role: Enterprise AI - Platform Engineer Location: New York City, Madison Square Park area Reports to: Head of Engineering Experience Level: 6+ years Work Model: Hybrid, with regular in-office presence Why Join Enterprise AI? Enterprise AI is building the platform that enables reliable deployment of AI systems across enterprise environments. As a Platform Engineer, you will build and operate the core of Turing’s Enterprise AI platform, including platform services, the model gateway, multi-tenant infrastructure, CI/CD, infrastructure as code, and observability. The central challenge is creating repeatable deployments across environments Turing may not control, including client cloud accounts, dedicated instances, and sovereign installations. As a Platform Engineer, You Will Build Core Platform Services Design, build, and operate the services that support the Enterprise AI platform, including platform infrastructure and the model gateway. Enable Enterprise Deployment Create reliable deployment systems for client cloud accounts, dedicated instances, and sovereign environments. Own Infrastructure and Delivery Systems Build and maintain CI/CD, infrastructure as code, and deployment tooling across environments using Kubernetes, Helm, Terraform or Pulumi, and GitOps practices. Build for Reliability Design systems that support secure multi-tenant operation, reliable service ownership, and clear operational accountability. Establish Observability Build observability across the platform using service-level objectives, structured logging, metrics, tracing, and alerting. Build FinOps Visibility Develop telemetry that tracks model and compute costs by tenant. The Anti-Sell Hands-on software engineering: This is not a cloud-operations or ticket-queue DevOps role. You must be able to write production software and contribute deeply to platform services. Operational ownership: You will participate in on-call operations and incident response and help build the processes required to operate a production platform. Deployment complexity: The platform must operate across client cloud accounts, dedicated instances, and sovereign environments. Team accountability: The platform must be observable, operable, and maintainable by the team rather than dependent on one individual. Our Enterprise AI Platform Engineers Typically Have Production platform engineering experience: Six or more years of platform or infrastructure engineering experience supporting production SaaS. Customer-controlled deployment experience: Experience shipping a product that customers run in their own environment, including in a VPC, a self-hosted environment, or an air-gapped environment. Strong software engineering depth: Deep expertise in at least one backend language, such as Go, Ruby, Python, or TypeScript, with the ability to read and work across the others. Cloud and infrastructure expertise: Deep experience in at least one cloud and comfort working in a second, including private networking, KMS, IAM, Kubernetes, Helm, Terraform or Pulumi, and GitOps. Reliability and observability ownership: Experience carrying a pager, running incidents, and working with tools such as OpenTelemetry, Prometheus, or Datadog. Data and platform systems experience: Familiarity with Postgres, object storage, queues, and vector stores. Candidates whose experience is limited to cloud operations or ticket-queue DevOps without software engineering, or who have never operated multi-tenant systems, will not be a fit for this role. Top-Tier Candidates Will Have Experience with model serving and model gateways, including vLLM, LiteLLM, or Triton. Experience implementing SOC 2 controls in CI. Experience deploying and operating AI systems in regulated or highly controlled environments. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: Limassol, Cyprus Workplace: hybrid We are looking for an Application Security Engineer in the AppSec team. As a Senior Application Security Engineer at inDrive, you will own whole areas of our AppSec program, from security requirements and design reviews to controls that work in production. You will work with product, platform and infrastructure teams on new architecture, AI systems and the security of our apps and services. The role fits a hands-on engineer who reviews code and architecture, leads work across several teams and makes security decisions based on data. Key Responsibilities Own one or more areas of the AppSec program end to end, including the plan, the risks and the metrics. Run security design reviews and threat modeling of new features, services and AI systems, and defend security requirements at the architecture committee. Assess the security of web, mobile and API applications and find variants of confirmed vulnerabilities across other services. Coordinate external security assessments and bug bounty findings, from triage and prioritisation with teams to fix verification. Design secure authentication and authorization between users, services and internal tools together with platform teams. Choose, pilot and tune security tools, and keep only those that pay off in real findings. Turn incidents and assessment results into lasting controls, policies and secure defaults for developers. Consult development teams on complex security issues and take shifts in AppSec duty for Critical and High incidents. Skills, Knowledge and Expertise Higher education in IT, preferably in information security. Knowledge of mobile application security on Android and iOS, client-server trust, app integrity and protection against bots and account takeover. Deep knowledge of web and API security, including authentication and authorization flaws, SSRF, injections and business logic issues, and the ability to find variants of a vulnerability class across many services. Strong understanding of identity and access, OAuth 2.0, OIDC, JWT, signing key rotation, mTLS, service-to-service authentication and SSO. Understanding of security risks of AI systems and LLM agents, such as prompt injection, excessive tool permissions and agent access to sensitive data, and practical use of AI in your own security work. Practical understanding of cloud-native infrastructure, Kubernetes, containers, API gateways and AWS. Experience in threat modeling and security design review of new features and architecture. Understanding of vulnerability management and risk, including severity, SLA, compensating controls and risk acceptance. Ability to read and review code in at least one language, preferably Golang. Track record of leading projects with two or more teams from a requirement to a verified fix, and clear written English. Experience with secrets and key management, rotation and response to credential leaks. Nice to have Knowledge of software supply chain security, dependencies, CI/CD pipelines and source control platform hardening. Experience tuning SAST, DAST and SCA tools for precision and judging a tool by noise and cost per real finding. Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Annual salary from 70 700 EUR gross. Final benefits may vary depending on the location.

Location: Cairo, Egypt Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Islamabad, Pakistan Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Almaty, Kazakhstan Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Limassol, Cyprus Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Help us challenge injustice by creating fair choices for millions of people across in 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Annual salary starting from 59 200 EUR gross Final benefits may vary depending on the location.