Senior Software Engineer, AI/ML Platform
New
A
Agility RoboticsRobotics, machine learning
All of our roles are U.S.-based.Full-TimeSenior
SalaryAnticipated Base Salary Range $197,000 — $307,000 USD
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Job Details
- Experience
- 5+ years of software engineering experience, with at least 2+ years working on ML infrastructure, data platforms or MLOps systems in production environments.
- Required Skills
- AWSGCPKubeflowMLFlowAirflowAzureTerraformMLOps
Requirements
- Have 5+ years of software engineering experience.
- Have at least 2+ years working on ML infrastructure, data platforms, or MLOps systems in production environments.
- Have experience building and maintaining ML platform components such as experiment tracking, model registries, training pipelines, or deployment systems.
- Be familiar with orchestration and tracking tools such as MLflow, WandB, Airflow, or Kubeflow.
- Be proficient with cloud-native platforms such as AWS, GCP, or Azure.
- Have experience with containers and infrastructure as code, such as CDK or Terraform.
- Have hands-on experience processing or modeling multimodal data, such as sensor logs, camera streams, or behaviour traces.
- Be comfortable collaborating with research scientists, data engineers, and robotics or autonomy teams.
- Robotics, autonomous vehicle, drone, or embedded ML experience is a bonus.
- Contributions to open-source ML infrastructure or MLOps tooling are a plus.
Responsibilities
- Design and implement the ML platform for data processing, training, evaluation, and deployment.
- Develop reliable workflows across cloud compute, Kubernetes, and continuous automation.
- Build core infrastructure components such as the model registry, feature store, and experiment tracking tools.
- Own developer-facing APIs and CLI tools for simple, reproducible ML workflows.
- Implement CI/CD for ML, including continuous retraining, automated testing, and model delivery to production.
- Work with AI researchers and robotics engineers to translate requirements into scalable systems.
- Partner with data platform engineers to integrate ML orchestration and metadata tracking with existing data lakes and pipelines.
- Apply MLOps practices for reproducibility, lineage, rollback, monitoring, and governance.
- Mentor junior engineers and contribute to the cloud platform roadmap and architecture discussions.
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