29 days ago
Pakistan (city not specified)Senior
Responsibilities
- Design, build, operate, monitor, and support backend services and internal tooling for the ML platform using Go and Python.
- Build deployment tooling and SDKs for model serving, autoscaling, and data capture.
- Integrate ML services with backend systems using synchronous and asynchronous communication while maintaining reliability under high load.
- Support the ML delivery lifecycle with data scientists and ML engineers, including model pipelines, monitoring, and performance metrics.
- Build end-to-end observability covering metrics, logs, dashboards, tracing, alerting, and runbooks.
- Create infrastructure as code with Terraform and Terragrunt and contribute through reviewed pull requests.
- Conduct technical research, design reviews, code reviews, and mentoring while maintaining engineering quality standards.
- Own services from design through production delivery and on-call support.
Requirements
- 5+ years of commercial backend engineering experience, including work on high-load systems.
- Strong production experience with Go and solid Python skills for data processing and service development.
- Strong SQL skills covering schema design, indexes, query optimization, replication, and partitioning, plus experience with Redis or similar key-value stores.
- Experience with HTTP, gRPC, Kafka, or RabbitMQ and reliability-focused service communication.
- Working knowledge of Docker, Kubernetes, cloud compute, storage, networking, and IAM services on AWS or GCP.
- Understanding of the ML lifecycle, including data preparation, model performance metrics, non-functional requirements, and concept drift.
- Experience designing and operating observability systems using tools such as Prometheus, Grafana, or ELK.
- Experience with Git workflows, code review, Linux command-line tools, and networking fundamentals including TCP/IP, DNS, VPCs, and load balancers.
- Ability to clearly explain technical trade-offs to data scientists, product owners, and on-call engineers.
- Preferred experience with Databricks, SageMaker, Kubeflow, MLflow, Airflow, GPU or LLM inference, vLLM, Triton, Langfuse, or infrastructure-as-code tools.
Benefits
- Mentoring, career consulting, and professional learning programs.
- International collaboration and participation in the Global Talent Exchange Program.
- Company-wide challenges, awards, sports activities, volunteering, and social-impact initiatives.
- Co-financed language courses and internal speaking clubs.
- Work location and final benefits vary depending on the location.
Tech Stack
Apache AirflowApache KafkaAWSDatabricksDockerGitGoGoogle Cloud PlatformGrafanagRPCKubernetesLinuxMLflowPrometheusPythonPyTorchRabbitMQRedisscikit-learnSQLTensorFlowTerraform
Categories
About inDrive
inDrive builds a global mobility and urban services marketplace best known for ride-hailing, with additional offerings in intercity rides, delivery, and financial services. The privately held company is headquartered in Mountain View, California, and operates across 48 countries, monetizing through fees on transactions in its app. Its platform emphasizes driver–rider price negotiation and has amassed hundreds of millions of downloads.