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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 Senior 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 5+ 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 Senior 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 5+ 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 Senior 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 5+ 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 Senior 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 5+ 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.

Location: Almaty, Kazakhstan Workplace: hybrid The Freight vertical provides essential logistics solutions for Private and SMB clients. This role covers the EMEA & APAC regions, with a strong operational focus on Kazakhstan as well as broader EMEA and APAC expansion . The key purpose of this role is to own and drive the development and scalable growth of the Freight business. As a Business Development Manager, you will be responsible for end-to-end business expansion — including commercial performance, pricing and unit economics, analytics, go-to-market strategy, and cross-functional execution. You will validate and scale growth hypotheses, ensure sustainable GMV and revenue growth, and build repeatable business models across your target markets. Key Responsibilities Own business growth and expansion of the Freight vertical across Kazakhstan, EMEA, and APAC, including revenue, GMV quality, and unit economics. Develop and manage pricing and tariff models, ensuring sustainable margins and competitive positioning. Own unit economics calculations and continuously optimize business performance. Drive analytical deep dives into business funnels (supply, demand, conversion, retention) and identify growth opportunities. Generate, validate, and scale business, product, marketing, and operational hypotheses in cross-functional teams. Own go-to-market initiatives, including branding, positioning, and collaboration with Marketing teams. Define and develop CRM logic: segmentation, lifecycle management, retention, and reactivation mechanics. Coordinate antifraud initiatives with risk and antifraud teams to ensure healthy marketplace economics. Conduct market and competitor analysis and contribute to long-term Freight vertical strategy. Own P&L for the region / vertical: planning, monitoring, and optimization of core metrics. Lead BD team and manage cross-functional stakeholders (Product, Marketing, Operations, Risk) to deliver business results. Qualifications 3+ years of experience in business development, commercial operations, or growth management in a marketplace or logistics/freight business. Advanced Excel / Google Sheets skills (financial models, unit economics, P&L modeling, tariff design, cohort analysis). Strong track record in funnel analysis, hypothesis testing, customer development (CustDev), and KPI monitoring. Direct experience with pricing, tariff modeling, CRM systems, and customer lifecycle management. Ability to independently analyze complex data, challenge assumptions with logic, and translate high-level strategy into clear execution frameworks. Strong execution and delivery mindset with the ability to work autonomously, decompose complex problems, and focus on high-impact initiatives. Excellent leadership and cross-functional stakeholder management skills. High level of empathy, resilience, and a constructive, low-toxicity approach to teamwork. Knowledge of SQL, Python, or A/B testing frameworks is a plus. Fluent English. 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. Final benefits may vary depending on the location.

Location: Almaty, Kazakhstan Workplace: onsite As the Sr. Talent Sourcer, you will drive candidate discovery, market intelligence, and pipeline strategy across key business functions. Key Responsibilities Act as the full owner of sourcing and candidate engagement strategies for active vacancies, driving pipeline quality and efficiency from start to finish while ensuring a positive experience for candidates and stakeholders. Own talent insights activities: manage stakeholder expectations anticipate potential hiring risks and build strong partnerships with recruiters, hiring managers, and cross-functional teams. Proactively analyze external labor market data - talent availability, competitor hiring activity, and compensation trends - to keep the company's talent acquisition strategy aligned with market dynamics. Play a key role in building strategic talent mapping in close collaboration with business leaders, recruitment teams, and PnC partners, including identifying hard-to-fill roles, emerging critical skills, key talent sources, and common career paths to priority positions. Actively develop and maintain professional talent networks and continuously improve the internal candidate database to support faster and more effective sourcing. Skills, Knowledge & Expertise 3+ years of talent sourcing, recruitment, or talent intelligence experience, preferably within high-growth, technology, or complex corporate environments. Proven track record of pipeline generation for niche, hard-to-fill, and executive-level positions. Demonstrated history of delivering data-driven market insights directly to senior leadership. Deep expertise in advanced sourcing techniques. Hands-on proficiency with modern ATS, CRM tools, and talent intelligence platforms. Ability to look beyond immediate vacancies to build sustainable, long-term talent pipelines. Strong interpersonal skills with the ability to influence hiring managers, manage expectations, and challenge assumptions using data. Passion for market research, data analysis, and translating conversion rates into actionable business recommendations. 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. Final benefits may vary depending on the location.

Location: Almaty, Kazakhstan Workplace: hybrid Geo Search runs the search that tens of millions of customers use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. We are building our own search and recommendation stack, which means we own the data, the ranking, and the measurement. The team is cross-functional, spanning backend, machine learning, mobile, and QA, and works with a product manager focused on relevance and with geo analysts. It owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, and its own search screens on Android and iOS. It is measured on order conversion, search conversion, and mean reciprocal rank We are looking for a Senior Data Scientist to own ranking, relevance and recommendation for geo search: the models that decide which places a customer sees, in what order, and how that changes with context. This is a modeling role with production responsibility, and it is close to a founding one. You will define how relevance is measured here, build the training data from scratch, and own the models that follow. The interesting constraint is that suggestions must return within tens of milliseconds of each keystroke, in cities where map data is thin and people type in mixed scripts, so model choice is an evidence-based trade-off you will own rather than a preference: gradient-boosted ranking on behavioral data today, transformers and large language models where they earn their place in query understanding and cross-script matching, and neural reranking if the failure analysis justifies it. Ground truth comes from what drivers, couriers and customers actually do, and from completed rides, so a large part of the craft is turning messy behavioral logs into labels you can trust. You will measure your impact through offline evaluation and online A/B tests, and changes reach millions of customers in weeks. Key Responsibilities Own ranking, relevance and recommendation for geo search end to end, from training data through to models serving in production Build the label pipeline that turns search sessions and completed rides into trustworthy training data, including correction for position bias and other presentation effects Train and own the models that order results, and the confidence model that decides per query whether we serve our own answer or fall back to an external provider Build query understanding for our markets, including cross-script matching, using large language models to label offline and distilling into models fast enough for the keystroke path Own evaluation end to end: the offline replay harness that scores recorded sessions against real rides, the error analysis that turns failures into work for the right team, and the online experiments that prove impact Keep models healthy after launch as data and cities shift Skills, Knowledge and Expertise Minimum qualifications 5 or more years building models that shipped and improved a production metric Direct experience with ranking, search relevance, or recommendation systems, including how they are evaluated Expert Python and SQL, fluency with gradient boosting, and working knowledge of transformers Experience taking a model to production and owning it afterwards The ability to work across teams, since search quality work generates data and engineering tasks that others own Preferred qualifications Depth in geocoding, autocomplete, or place search Learning to rank in practice: pairwise and listwise objectives, MRR, NDCG, Hit@k, and their failure modes Relevance tuning on OpenSearch or Elasticsearch, including analyzers Multilingual or cross-script search, for example Arabic and Arabizi or Urdu and Roman Urdu Distilling language models under a latency budget, and experience in mapping or geospatial products in developing markets Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 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 Geo Search runs the search that tens of millions of customers use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. We are building our own search and recommendation stack, which means we own the data, the ranking, and the measurement. The team is cross-functional, spanning backend, machine learning, mobile, and QA, and works with a product manager focused on relevance and with geo analysts. It owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, and its own search screens on Android and iOS. It is measured on order conversion, search conversion, and mean reciprocal rank We are looking for a Senior Data Scientist to own ranking, relevance and recommendation for geo search: the models that decide which places a customer sees, in what order, and how that changes with context. This is a modeling role with production responsibility, and it is close to a founding one. You will define how relevance is measured here, build the training data from scratch, and own the models that follow. The interesting constraint is that suggestions must return within tens of milliseconds of each keystroke, in cities where map data is thin and people type in mixed scripts, so model choice is an evidence-based trade-off you will own rather than a preference: gradient-boosted ranking on behavioral data today, transformers and large language models where they earn their place in query understanding and cross-script matching, and neural reranking if the failure analysis justifies it. Ground truth comes from what drivers, couriers and customers actually do, and from completed rides, so a large part of the craft is turning messy behavioral logs into labels you can trust. You will measure your impact through offline evaluation and online A/B tests, and changes reach millions of customers in weeks. Key Responsibilities Own ranking, relevance and recommendation for geo search end to end, from training data through to models serving in production Build the label pipeline that turns search sessions and completed rides into trustworthy training data, including correction for position bias and other presentation effects Train and own the models that order results, and the confidence model that decides per query whether we serve our own answer or fall back to an external provider Build query understanding for our markets, including cross-script matching, using large language models to label offline and distilling into models fast enough for the keystroke path Own evaluation end to end: the offline replay harness that scores recorded sessions against real rides, the error analysis that turns failures into work for the right team, and the online experiments that prove impact Keep models healthy after launch as data and cities shift Skills, Knowledge and Expertise Minimum qualifications 5 or more years building models that shipped and improved a production metric Direct experience with ranking, search relevance, or recommendation systems, including how they are evaluated Expert Python and SQL, fluency with gradient boosting, and working knowledge of transformers Experience taking a model to production and owning it afterwards The ability to work across teams, since search quality work generates data and engineering tasks that others own Preferred qualifications Depth in geocoding, autocomplete, or place search Learning to rank in practice: pairwise and listwise objectives, MRR, NDCG, Hit@k, and their failure modes Relevance tuning on OpenSearch or Elasticsearch, including analyzers Multilingual or cross-script search, for example Arabic and Arabizi or Urdu and Roman Urdu Distilling language models under a latency budget, and experience in mapping or geospatial products in developing markets Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 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. Annual salary from 70 700 EUR gross Final benefits may vary depending on the location.

Location: Lima, Peru Workplace: hybrid We are looking for a data-driven Senior Product Marketing Manager to own the strategy, execution, and continuous optimization of our customer lifecycle and product engagement initiatives. Key Responsibilities Campaign Preparation and Execution: Independently create, schedule, and execute marketing campaigns (email, push notifications, WhatsApp, in-app messages, banners) across multiple customer segments. Optimize campaigns for delivery across time zones, devices, and customer preferences. Ensure compliance with legal standards such as GDPR and other regional requirements. Audience Segmentation: Design and implement custom segmentation strategies based on multidimensional criteria such as behavior, demographics, orders and ride history, and engagement levels. Maintain dynamic customer segments that update automatically as data changes. Trigger Campaign Configuration: Build and manage multi-step automated workflows, such as re-engagement, loyalty program adoption, or upselling campaigns, with minimal supervision. Test and troubleshoot triggers to ensure proper execution and alignment with the customer lifecycle. Testing and Optimization: Plan, execute, and document A/B and multivariate tests for key campaign elements, including design, timing, and messaging. Use insights from testing to implement data-driven improvements across campaigns. Perform regular performance reviews to refine optimization strategies. Reporting and Analysis: Generate detailed reports on campaign performance, interpreting metrics like open rates, CTRs, conversions, and ROI. Present data insights to stakeholders and provide actionable recommendations to improve rider and driver engagement. Collaboration and Mentoring: Collaborate with cross-functional teams (content, design, analytics) to deliver campaigns. Provide guidance and support to junior CRM specialists, including peer reviews and knowledge sharing. Skills, Knowledge & Expertise CRM Platform Proficiency: Independently execute at least five end-to-end campaigns quarterly using advanced CRM platform features (e.g., Bloomreach, Salesforce Marketing Cloud, HubSpot). Build at least three advanced automations quarterly, such as multi-step workflows with branching logic (e.g., loyalty or churn prevention campaigns). Complete at least two courses with intermediate certification for Bloomreach or demonstrate equivalent expertise. Data Analysis and Reporting: Create detailed reports with data visualizations for at least five campaigns quarterly, incorporating metrics like open rates, conversion rates, and ARPPU. Work with datasets using tools like Excel or Google Sheets with advanced functions (e.g., INDEX/MATCH, QUERY). Present campaign insights to stakeholders in three formal presentations with actionable recommendations. Digital Marketing Expertise: Apply intermediate-level knowledge of digital marketing strategies, including personalization, retargeting, and customer journey mapping. Assist in the strategic planning of at least two multi-channel campaigns integrating email, WhatsApp, in-app, banners, and push notifications. Advanced Data Handling: Segment audiences dynamically based on real-time data, using at least three different criteria simultaneously. Ensure data compliance by implementing processes that align with GDPR/CCPA requirements for all campaigns. English Proficiency: Write comprehensive campaign briefs and reports in English, ensuring accuracy and clarity. Lead or co-lead three cross-functional meetings in English, presenting updates or insights confidently. Advanced Testing and Optimization: Conduct and analyze A/B tests for at least five campaigns quarterly, including testing subject lines, offers, layouts, and timing. Implement changes based on test results, achieving measurable improvements in key KPIs (e.g., a 5 statistically significant lift in conversion). Communication Deliverability: Monitor and resolve deliverability issues for campaigns. Use tools like Postmaster Tools or delivery reports to troubleshoot spam issues and optimize sender reputation. 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. Final benefits may vary depending on the location.

Location: Pretoria, South Africa Workplace: hybrid We are looking for a Senior Marketing Manager to drive passenger acquisition, retention, and brand growth for inDrive Southern Africa region with a primary focus on South Africa. You will work closely with the Regional Marketing Lead and the local team to bring inDrive's global brand strategy to life on the ground – leading hands-on execution of marketing activations, partnerships, and brand-building initiatives across the region. Key Responsibilities Execute locally relevant marketing strategy by adapting inDrive's global brand strategy and guidelines to the South Africa, Zambia, Zimbabwe and Botswana markets Work as an embedded, hands-on member of the local team, partnering closely with Business Development, Driver Operations, and other local functions within a matrix structure Contribute to and help develop marketing briefs for advertising campaigns and creative production, ensuring creatives are culturally relevant Plan and deliver 360° marketing campaigns, activations, and partnerships that drive passenger acquisition and brand awareness Partner with the Regional Marketing Lead on budget and strategic planning, and execute the approved local marketing budget Analyze the acquisition and retention funnels, surface product improvement opportunities, and conduct in-market customer development (CustDev) research Generate and test performance marketing hypotheses in coordination with the in-house performance marketing team Skills, Knowledge & Expertise 4+ years of digital consumer marketing experience, ideally within a global or multinational company Hands-on experience planning and executing multi-channel (360°) marketing campaigns and activations Experience working with influencer marketing and partnerships Comfortable operating in a matrix structure, collaborating with and influencing multiple stakeholders and functions without direct authority An execution-focused mindset – equally comfortable shaping plans and personally delivering the work, including agency management Strong analytical skills, with the ability to assess campaign and funnel performance and translate data into action Experience working with a mobile product is a plus but not required 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. Final benefits may vary depending on the location.

Location: Tbilisi, Georgia Workplace: hybrid Geo Search runs the search that tens of millions of customers use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. We are building our own search and recommendation stack, which means we own the data, the ranking, and the measurement. The team is cross-functional, spanning backend, machine learning, mobile, and QA, and works with a product manager focused on relevance and with geo analysts. It owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, and its own search screens on Android and iOS. It is measured on order conversion, search conversion, and mean reciprocal rank We are looking for a Senior Data Scientist to own ranking, relevance and recommendation for geo search: the models that decide which places a customer sees, in what order, and how that changes with context. This is a modeling role with production responsibility, and it is close to a founding one. You will define how relevance is measured here, build the training data from scratch, and own the models that follow. The interesting constraint is that suggestions must return within tens of milliseconds of each keystroke, in cities where map data is thin and people type in mixed scripts, so model choice is an evidence-based trade-off you will own rather than a preference: gradient-boosted ranking on behavioral data today, transformers and large language models where they earn their place in query understanding and cross-script matching, and neural reranking if the failure analysis justifies it. Ground truth comes from what drivers, couriers and customers actually do, and from completed rides, so a large part of the craft is turning messy behavioral logs into labels you can trust. You will measure your impact through offline evaluation and online A/B tests, and changes reach millions of customers in weeks. Key Responsibilities Own ranking, relevance and recommendation for geo search end to end, from training data through to models serving in production Build the label pipeline that turns search sessions and completed rides into trustworthy training data, including correction for position bias and other presentation effects Train and own the models that order results, and the confidence model that decides per query whether we serve our own answer or fall back to an external provider Build query understanding for our markets, including cross-script matching, using large language models to label offline and distilling into models fast enough for the keystroke path Own evaluation end to end: the offline replay harness that scores recorded sessions against real rides, the error analysis that turns failures into work for the right team, and the online experiments that prove impact Keep models healthy after launch as data and cities shift Skills, Knowledge and Expertise Minimum qualifications 5 or more years building models that shipped and improved a production metric Direct experience with ranking, search relevance, or recommendation systems, including how they are evaluated Expert Python and SQL, fluency with gradient boosting, and working knowledge of transformers Experience taking a model to production and owning it afterwards The ability to work across teams, since search quality work generates data and engineering tasks that others own Preferred qualifications Depth in geocoding, autocomplete, or place search Learning to rank in practice: pairwise and listwise objectives, MRR, NDCG, Hit@k, and their failure modes Relevance tuning on OpenSearch or Elasticsearch, including analyzers Multilingual or cross-script search, for example Arabic and Arabizi or Urdu and Roman Urdu Distilling language models under a latency budget, and experience in mapping or geospatial products in developing markets 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. Final benefits may vary depending on the location.

Location: Limassol, Cyprus Workplace: hybrid We are looking for a Staff Product Manager to shape this product from an early stage and lead it through validation, launch and international scale. We are building a new product that enables people across inDrive’s global user base to earn additional income by completing tasks that generate high-quality training data for AI. Following a successful pilot, we are moving from a partner-led solution towards our own scalable platform. It will connect demand from AI companies with distributed data collection and labelling, covering the full journey from buyer requirements and task design to contributor payouts, quality assurance and dataset delivery. Key Responsibilities Own the end-to-end product strategy, roadmap and outcomes for the Data Tasks platform. Translate data buyer requirements into scalable collection, annotation and quality-control workflows. Design the full product journey across both sides of the platform: enterprise buyers and task contributors. Build capabilities for task creation and distribution, contributor onboarding, payouts, data submission, validation, labelling and dataset delivery. Define the right balance between building internal capabilities, using external platforms and working with specialised partners. Establish product metrics across demand, supply, data quality, unit economics and contributor experience. Run discovery and experiments to improve acquisition, activation, task completion, retention and payout experience. Work closely with Commercial, Engineering, Data, Operations, Legal, Privacy, Information Security and market teams. Ensure the platform can support multiple data types, including audio, text, images, video and multimodal data, across different countries and use cases. Skills, Knowledge and Expertise Significant product management experience, including ownership of complex products at Staff, Principal or equivalent level. Proven experience building and scaling a new product or platform from an early stage. Strong systems thinking and the ability to design end-to-end products involving multiple users, workflows and operational processes. Experience with B2B platforms, marketplaces, crowdsourcing, data products, AI/ML infrastructure or workflow products. Strong commercial judgement and the ability to connect customer needs, product decisions and unit economics. Ability to work with enterprise customers and translate technical or specialised requirements into scalable product solutions. Strong analytical skills and experience defining metrics, running experiments and making decisions under uncertainty. Ability to operate independently, bring clarity to ambiguity and influence senior stakeholders without relying on formal authority Fluent English and clear written and verbal communication skills. Nice to have Experience with AI training data, data collection, annotation, model evaluation or quality-assurance systems. Experience building two-sided marketplaces or products involving distributed contributor networks. Experience launching products across emerging markets and adapting them to local payment, legal and operational constraints. 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 90 700 EUR gross. Final benefits may vary depending on the location.

Location: Almaty, Kazakhstan Workplace: hybrid The QA Load team is responsible for the performance and scalability of services across their entire lifecycle — from Go/No-Go decisions before launch to working with real production traffic. We design and execute load tests, model real-world usage scenarios, identify and eliminate bottlenecks in backend systems, databases, messaging, and infrastructure, and work closely with SRE, DevOps, and product engineering teams. The role focuses on systematic performance improvement: building load-testing infrastructure, automation and CI/CD quality gates, performance analysis and experimentation, and translating technical metrics (latency, RPS, p95/p99) into clear business impact. Our goal is to make performance a core aspect of product quality and ensure the stable operation of high-load, geo-distributed systems. Key Responsibilities Performance Audits & Integration Support: Go/No-Go decisions with our stakeholder about launches services before integrating them into our ecosystem and integrations support (audit APIs, web, databases, caches, message brokers) Design&Execute Load Tests global strategy: Modeling of load testing scenarios (load, stress, soak, peak, find max tests), methodology and formation of non-functional requirements for system using k6, JMeter (DSL framework) Simulate real-world scenarios and traffic patterns to validate systems under production-like conditions (including high RPS, concurrency levels, data skew, and geo-distributed load) Load Modeling: Calculate and validate detailed load models – e.g. expected RPS, concurrent users, transaction volumes, and data distribution. Adjust these models to mirror peak and edge-case scenarios, ensuring our capacity planning is grounded in reality. Bottleneck Analysis & Fixes: Find, analyze, and fix performance bottlenecks. This includes: backend issues (CPU hotspots, memory leaks, GC pauses, thread pool exhaustion) DB inefficiencies (Aurora MySQL/RDS indexing, query locks, IOPS limits) messaging/queue back-pressure (Kafka partitioning, consumer lag, throughput limits) network constraints (latency, load balancer settings, timeouts) Work hands-on with SRE, DevOps, and Backend teams to implement optimizations and verify the improvements. Prepare for Production Traffic: Ensure services are production-ready. Drive capacity planning exercises and stress tests to determine scaling lag/limits and failure modes. Verify that services can gracefully handle traffic spikes or component failures without violating SLAs or SLOs. Help partners configure autoscaling policies for sustained performance Loadtesting Infrastructure Setup&Tuning: Set up, audit, and operate performance testing infrastructure for our services load testing automation and geo-distributed load generator to simulate production traffic Performance Analysis & Experimentation: Approach performance issues with an performance analyst/architect mindset – form hypotheses, design experiments, run tests, and draw conclusions to configuration infrastructure and fix backend code. For example, if latency is high, hypothesize the cause (e.g. database index missing), test the hypothesis, and then make a the fix. Repeat this iterative process to methodically improve system performance. Business Impact Translation: Translate technical performance insights into business risks and opportunities. For instance, demonstrate how reducing latency or improving p95 response time can increase conversion rates or GMV. Communicate these insights to both engineers and non-technical stakeholders to underline the business value of performance work. Observability & Monitoring: Utilize observability tools to monitor performance during tests. Set up dashboards and alerts in Grafana/Prometheus and other systems (e.g. New Relic, Dynatrace, Zabbix, ELK Stack, Coroot), analyze traces and logs. Use few methods to interpret results and guide troubleshooting. Performance Tooling: Build internal performance tooling in Golang: Design and maintain internal tools for automation performance testing and analysis Load as a Service for product teams: traffic generators, load managers, mocks, test stubs, data feeders, and helper services used across partner integrations. Building load test scenarios to Jmeter (DSL framework) and k6 in JavaScript. Innovate on tooling to make load tests easier to create, run, and analyze for the whole team. CI/CD Integration: Integrate performance tests into our CI/CD pipeline as QG. Automate load tests run to every new integration or code change is evaluated for performance regressions. Ensure that build pipelines can catch and flag performance drops (e.g. increased response times, higher resource usage) before changes reach production. Reporting&Communication: Prepare executive-ready performance reports for engineering teams and C-level stakeholders. Highlight key metrics (p95/p99 latency, throughput, error rates, saturation signals) and identify primary scalability bottlenecks across services, databases, caches, and messaging systems, and compare results against SLOs and capacity targets. Provide clear root-cause explanations and actionable recommendations, including performance vs cost trade-offs, to support production readiness and Go/No Go decisions. Performance Culture: Champion a culture where performance = product quality. Educate and influence teams to treat performance testing as an integral, continuous part of the development lifecycle rather than a one-time task. Share best practices and success stories, ensuring that performance considerations are baked into design, coding, and testing processes from the start. Conducting production load and traffic validation experiments with full ownership of risk assessment, blast radius control, observability, and rollback procedures. Work with geo-distributed, high-load systems (200k+ RPS) and latency-sensitive mobile flows Set up production-like performance environments in AWS Conducting and modeling load tests in production Performance analytics → hypotheses → experiments → fixes → measurable business impact (latency, conversion, GMV) Skills, Knowledge and Expertise 5+ years in Performance/SRE/Load Testing Strong programming skills in Golang/Kotlin/Java Strong k6 or JMeter DSL - Performance Testing Mastery: Hands-on expertise with modern load testing tools, especially k6 and/or JMeter (and their scripting DSL). Deep understanding of performance best practice – you know when to apply load testing vs stress testing vs soak testing, and how to interpret the results of each. Hands-on experience with AWS infrastructure on for high-load systems: EKS, EC2, Auto Scaling Groups, ALB/NLB, VPC, subnets, routing, security groups Designing and tuning autoscaling strategies for compute (KEDA/Karpenter/Cluster Autoscaler, EC2 scaling policies) Understanding network performance characteristics: latency, cross-AZ traffic, NAT/egress limits, load balancer behavior under high RPS Profiling Golang, Kafka, Redis, DB tuning experience Automation loadtesting and make QG (JVM languages and Go) SRE mindset (RED/USE, SLO, error budgets) 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. Final benefits may vary depending on the location.

Location: Remote; New York; Washington, D.C. ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $22B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. ABOUT THE ROLE We’re looking for an experienced partnerships leader to own and scale ElevenLabs’ strategic relationships with cloud hyperscalers and Systems Integrators, the companies whose platforms, field teams and delivery capacity sit at the heart of how enterprises buy and deploy voice and customer experience AI. You’ll build and manage co-sell and marketplace programmes with AWS, Microsoft Azure and Google Cloud, and build joint GTM and delivery motions with Regional and Global Systems Integrators. The goal: get ElevenLabs deeply embedded in hyperscaler partner networks and SI practices, so that when enterprises modernize their contact centers or build on cloud infrastructure, ElevenLabs is the natural AI voice layer, and partners have both the incentive and the capacity to sell and deliver it. This is a 0→1 build in a high-leverage category. You’ll design co-sell and SI GTM frameworks, drive pipeline with hyperscaler field teams, structure marketplace and private offer motions, negotiate referral, resell and joint-offering agreements with SIs, and own the revenue and pipeline outcomes that come from these relationships. WHAT SUCCESS LOOKS LIKE (FIRST 6-9 MONTHS) - Partner-sourced and influenced revenue: closed-won ARR attributable to hyperscaler and SI relationships - Hyperscaler marketplace and co-sell presence: listings live, co-sell registrations flowing, and active motions running with hyperscaler field teams - SI practice activation: named SI practice leads, certified practitioners and at least one joint offering or solution play in market - Partner-sourced pipeline: qualified opportunities sourced or co-sold through hyperscaler and SI field teams each quarter - Field activation: measurable engagement with hyperscaler sellers and SI client partners and delivery teams, not just BD or alliance leads, translating to warm intros and joint account planning REQUIREMENTS - 8+ years in technology partnerships, alliances, or GTM roles within SaaS or cloud, with direct experience running hyperscaler partner programmes and SI/GSI partnerships - Hands-on experience with hyperscaler co-sell programmes (AWS ISV Accelerate and ACE, Microsoft AI Cloud Partner Programme, Google Cloud Partner Advantage or equivalent) and marketplace operations (listings, private offers, commit burndown). You know how field co-sell actually works, not just the theory - Working knowledge of the SI ecosystem: relationships with practice leads and client partners at GSIs and RSIs, and an understanding of how SIs build practices, attach services to software ARR, and allocate delivery capacity - A builder mindset: experience creating co-sell and SI motions from 0→1, rather than inheriting a mature programme - Comfortable with technical, API-driven products; able to work with solutions engineers to scope solution architectures and articulate ElevenLabs’ value to technical partner audiences - Experience building enablement, certification or reference architectures that activate partner field and delivery teams at scale - Based in North America - Bonus: background combining 3-4 years in a quota-carrying sales or solutions role with 3-4 years in partnerships or alliances; prior experience at a company that successfully scaled through cloud marketplace and SI channels LOCATION This role is remote-first, so it can be executed from anywhere in North America, with the ability to operate in US Eastern and Pacific timezones. If you prefer, you can work from our offices in New York or San Francisco. #LI-Remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

Location: New York, NY (HQ) Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role Ramp is building the Private Equity channel into one of the company's most significant sources of enterprise growth. Sponsors and their operating teams shape how portfolio companies run finance, and when they standardize on a platform, they do it across dozens of companies at once. This role will be focused on managing the largest funds in the market. You will own a book of the most strategic PE firms Ramp works with, many of which have multiple investment investment strategies, fund vehicles, and GP stakes businesses under a single name. Each one likely has a different operating partner for each strategy, a deal team with its own relationships, and portfolio CFOs who make the final call. Your job is to know all of them, keep every thread moving, and turn that access into measurable adoption across their portfolios. It is a high-volume, high-stakes book. The work is relentless relationship management paired with genuine commercial discipline, and it carries the highest CP goal on the channel team. There are only a handful of these seats at Ramp, and real executive visibility comes with them. What You'll Do - Own a book of upmarket and megafund PE partners, serving as their primary relationship owner at Ramp. - Multi-thread across each firm: operating partners by strategy, deal teams, platform and portfolio operations leaders, and portfolio-company CFOs. - Run disciplined portfolio analysis to find where Ramp can create value, and work with Account Executives to convert that into pipeline. - Generate and own qualified pipeline against an aggressive CP target, prioritizing the large opportunities that move the number. - Negotiate custom partnership structures where they make sense, including adoption commitments, and hold the line where terms do not work for Ramp. - Partner with Events, Growth, Marketing, and Finance to deliver sponsorships, webinars, executive dinners and the collateral your partners ask for. - Build and maintain the operating system behind the book: relationship maps, portfolio analysis, follow-ups and next steps across a large partner set on a tight touch cycle - Use AI to automate research, portfolio mapping, call prep and follow-through so the book scales beyond what manual effort allows What You Need - A track record of carrying and hitting a high quota in a commercial role. Outbound sales development, account executive, or partnerships backgrounds all work. - Deep working knowledge of how a private equity firm is structured, including the distinct roles of operating partners, deal teams, and portfolio leadership. - Demonstrated ability to build and expand relationships across a large, complex organization. - Exceptional organization and agency. History of managing a large book without dropping threads, continual follow-up, and navigating complex organizations. - Comfort negotiating directly with sophisticated counterparts, and the judgment to know when to walk away. - A practical, self-directed approach to AI. You build your own workflows rather than waiting for tooling. - Strong cross-functional instincts across events, growth, marketing and finance. Ability to work dynamically with others internally to drive results. - Willingness to travel frequently and to work from our New York office. CT or NJ with an in-office commitment also works, and we are open to relocation Nice to Haves - Experience partnering with or selling to private equity firms and their portfolio companies. - Experience at a company with an established channel sales motion. - Fintech, payments, expense management, or CFO-software background. BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Location: Barcelona, Spain; Tokyo, Japan; Portland, OR Workplace: onsite About Welocalize  Welocalize, a Welo Global brand, serves localization teams through AI-enabled multilingual content solutions that enable enterprises to operate and scale globally. Welocalize combines AI, automation, and human expertise to support enterprises in more than 300 languages, enabling accurate, culturally aligned, and compliant multilingual content at scale.   Welocalize’s Opal Platform comprises patented technology designed to automate multilingual content and improve workflow performance for enterprises. Opal functions as an agentic system that orchestrates AI, automation, and human expertise to coordinate content workflows across the lifecycle, improving speed, scalability, and operational control for global enterprises. These solutions operate within a secure and compliant environment supported by seven ISO certifications. Welocalize is headquartered in New York with offices worldwide. welocalize.com  ROLE OVERVIEW  Welocalize is looking for a native-level Swahili speaker to join a confidential, large-scale AI and consumer technology programme for a major global technology and communications client.  It's a hands-on, on-site role that combines two things: evaluating a pre-release AI model in your language, and testing how the product actually performs on a client-supplied device — a smartphone and a next-generation smart glass. If you enjoy noticing the detail other people miss, this is work you'll find satisfying.  Welocalize is looking for a native-level Swahili speaker to join a large-scale AI and consumer technology programme.  It's a hands-on, on-site role that combines two things: evaluating a pre-release AI model in your language, and testing how it performs on a real device. You'll be among the first people to see this technology in Swahili, and your judgement shapes how well it works before anyone else uses it.  If you enjoy noticing the detail other people miss, this is work you'll find satisfying.    MAIN RESPONSIBILITIES  Review pre-release AI model output in Swahili and judge it on accuracy, naturalness, appropriateness, and overall quality — would a native speaker really say it this way? This includes fact-checking each response against the source material.

Location: Barcelona, Spain; Tokyo, Japan; Portland, OR Workplace: onsite About Welocalize  Welocalize, a Welo Global brand, serves localization teams through AI-enabled multilingual content solutions that enable enterprises to operate and scale globally. Welocalize combines AI, automation, and human expertise to support enterprises in more than 300 languages, enabling accurate, culturally aligned, and compliant multilingual content at scale.   Welocalize’s Opal Platform comprises patented technology designed to automate multilingual content and improve workflow performance for enterprises. Opal functions as an agentic system that orchestrates AI, automation, and human expertise to coordinate content workflows across the lifecycle, improving speed, scalability, and operational control for global enterprises. These solutions operate within a secure and compliant environment supported by seven ISO certifications. Welocalize is headquartered in New York with offices worldwide. welocalize.com  ROLE OVERVIEW  Welocalize is looking for a native-level Swahili speaker to join a confidential, large-scale AI and consumer technology programme for a major global technology and communications client.  It's a hands-on, on-site role that combines two things: evaluating a pre-release AI model in your language, and testing how the product actually performs on a client-supplied device — a smartphone and a next-generation smart glass. If you enjoy noticing the detail other people miss, this is work you'll find satisfying.  Welocalize is looking for a native-level Swahili speaker to join a large-scale AI and consumer technology programme.  It's a hands-on, on-site role that combines two things: evaluating a pre-release AI model in your language, and testing how it performs on a real device. You'll be among the first people to see this technology in Swahili, and your judgement shapes how well it works before anyone else uses it.  If you enjoy noticing the detail other people miss, this is work you'll find satisfying.    MAIN RESPONSIBILITIES  Review pre-release AI model output in Swahili and judge it on accuracy, naturalness, appropriateness, and overall quality — would a native speaker really say it this way? This includes fact-checking each response against the source material.

Location: Barcelona, Spain; Tokyo, Japan; Portland, OR Workplace: onsite About Welocalize  Welocalize, a Welo Global brand, serves localization teams through AI-enabled multilingual content solutions that enable enterprises to operate and scale globally. Welocalize combines AI, automation, and human expertise to support enterprises in more than 300 languages, enabling accurate, culturally aligned, and compliant multilingual content at scale.   Welocalize’s Opal Platform comprises patented technology designed to automate multilingual content and improve workflow performance for enterprises. Opal functions as an agentic system that orchestrates AI, automation, and human expertise to coordinate content workflows across the lifecycle, improving speed, scalability, and operational control for global enterprises. These solutions operate within a secure and compliant environment supported by seven ISO certifications. Welocalize is headquartered in New York with offices worldwide. welocalize.com  ROLE OVERVIEW  Welocalize is looking for a native-level Swahili speaker to join a confidential, large-scale AI and consumer technology programme for a major global technology and communications client.  It's a hands-on, on-site role that combines two things: evaluating a pre-release AI model in your language, and testing how the product actually performs on a client-supplied device — a smartphone and a next-generation smart glass. If you enjoy noticing the detail other people miss, this is work you'll find satisfying.  Welocalize is looking for a native-level Swahili speaker to join a large-scale AI and consumer technology programme.  It's a hands-on, on-site role that combines two things: evaluating a pre-release AI model in your language, and testing how it performs on a real device. You'll be among the first people to see this technology in Swahili, and your judgement shapes how well it works before anyone else uses it.  If you enjoy noticing the detail other people miss, this is work you'll find satisfying.    MAIN RESPONSIBILITIES  Review pre-release AI model output in Swahili and judge it on accuracy, naturalness, appropriateness, and overall quality — would a native speaker really say it this way? This includes fact-checking each response against the source material.