Senior Software Engineer, Workflow Platforms

Botauto; Houston (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

Botauto in Houston is seeking a Senior Software Engineer, Workflow Platforms, focused on architecting, building, and operating workflow orchestration systems. In this role, you will utilize platforms such as Argo Workflows and Airflow, ensuring reliable and scalable systems for various workloads.

The ideal candidate has over 5 years of experience in platform engineering and strong skills in software development including Python, Go, or Java. A Bachelor's degree or equivalent experience is also required.

Qualifications

• 5+ years of hands-on experience in platform engineering, infrastructure, DevOps, or SRE roles.
• Significant experience with workflow orchestration platforms such as Argo Workflows, Airflow, or comparable systems.
• Solid understanding of Kubernetes and distributed systems.

Responsibilities

• Architect and operate workflow orchestration platforms supporting various workloads.
• Build internal platforms and self-service tooling on top of orchestration engines.
• Ensure reliability and performance of workloads through observability and resource optimization.

Skills

Platform engineering

DevOps

Kubernetes

Software development (Python, Go, Java, or JavaScript/TypeScript)

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Tools

Argo Workflows

Airflow

Terraform

Kubernetes

Job description

We are seeking a highly skilled and motivated Senior Software Engineer, Workflow Platforms to architect, build, and operate the workflow orchestration platforms that power Bot Auto’s engineering and autonomy workloads. From simulation and machine learning training to data pipelines and CI/CD, our teams depend on reliable, scalable workflow systems to move fast. In this role, you will own one or a hybrid of orchestration platforms (such as Argo Workflows and Airflow), operate them at scale, and develop the internal platforms and abstractions built on top of them that make running complex workloads simple, observable, and cost‑efficient.

Key Responsibilities

• Architect, deploy, and operate workflow orchestration platforms (e.g., Argo Workflows, Airflow, or a hybrid) supporting simulation, machine learning and model training, data pipelines, CI/CD, and other general-purpose workloads.
• Build internal platforms, abstractions, SDKs, and self-service tooling on top of orchestration engines to make authoring, running, and monitoring workflows simple and reliable for engineers.
• Operate workflow platforms at scale on Kubernetes across cloud (AWS) and on-prem data center environments, handling scheduling, autoscaling, GPU and heterogeneous resources, and cross-cluster orchestration.
• Ensure reliability, performance, and cost efficiency of workloads through observability, queuing and prioritization, retries, and resource optimization.
• Partner with ML, simulation, data, and infrastructure teams to understand workload requirements and deliver fit‑for‑purpose pipelines.
• Integrate workflow platforms with storage, data streaming and event systems, artifact and model registries, and CI/CD tooling.
• Establish best practices, templates, and documentation for workflow authoring and operations; mentor engineers across the company.
• Handle user‑impacting issues promptly with clear communication — mitigate in the short term and follow up with durable long‑term solutions.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience
• 5+ years of hands‑on experience in platform engineering, infrastructure, DevOps, or SRE roles
• Significant experience with workflow orchestration platforms such as Argo Workflows, Airflow, or comparable systems
• Strong software development skills in one or more languages: Python, Go, Java, or JavaScript/TypeScript
• Solid understanding of Kubernetes and distributed systems

Preferred Qualifications

• Expert‑level experience operating Argo Workflows, Airflow, and/or other engines (e.g., Prefect, Dagster, Temporal, Kubeflow Pipelines, Flyte)
• Experience orchestrating ML training, simulation, or large‑scale data and batch workloads, including GPU scheduling
• In‑depth Kubernetes experience (EKS, GKE, AKS, RKE2/Rancher) and cross‑cluster orchestration
• IaC tools proficiency, including Terraform, Pulumi, OpenTofu, or Ansible
• Experience with data streaming and event platforms, including NATS JetStream, Kafka, Pulsar, or RabbitMQ
• Familiarity with observability stacks: Prometheus, Grafana, Loki, OpenTelemetry, or comparable
• Demonstrated ability to optimize workload cost and performance without compromising reliability