Senior Software Engineer, AI/ML Platform
New
J
JobgetherRobotics, AI/ML
Based in the United StatesFull-TimeSenior
Salary$197,000–$307,000 USD
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Job Details
- Experience
- 5+ years of professional software engineering experience, including at least 2+ years building or operating production ML infrastructure, data platforms, or MLOps systems
- Required Skills
- PythonCloud ComputingKubernetesMLFlowData engineeringCI/CDTerraformMLOps
Requirements
- 5+ years of professional software engineering experience, including at least 2+ years building or operating production ML infrastructure, data platforms, or MLOps systems.
- Hands-on experience developing modern ML platform components such as experiment tracking, model registries, training pipelines, deployment systems, or related infrastructure.
- Familiarity with ML orchestration and experiment management technologies such as MLflow, Weights & Biases, Airflow, Kubeflow, or comparable tools.
- Strong experience with cloud-native platforms such as AWS, Google Cloud, or Azure, along with containers and Infrastructure as Code technologies such as Terraform or CDK.
- Experience processing or modeling multimodal datasets, including sensor logs, camera streams, behavioral traces, or similar robotics and machine-learning data.
- Strong software engineering fundamentals and the ability to build reliable, maintainable, production-grade systems.
- Experience collaborating with research scientists, data engineers, robotics teams, or autonomy engineers to deliver infrastructure used by technical teams.
- Strong understanding of CI/CD, automation, observability, reproducibility, and scalable platform architecture.
- Excellent communication and collaboration skills.
Responsibilities
- Design and implement the ML platform that orchestrates the complete AI lifecycle, including data processing, training, evaluation, deployment, and monitoring.
- Develop reliable and scalable workflows across cloud infrastructure, Kubernetes, and continuous automation environments.
- Build foundational ML infrastructure components such as model registries, feature stores, experiment tracking systems, and model management tooling.
- Create developer-facing APIs, command-line tools, and reusable infrastructure that make machine learning workflows simple, reproducible, and accessible to engineering and research teams.
- Implement CI/CD capabilities for ML workflows, supporting continuous retraining, automated testing, standardized model packaging, and reliable production delivery.
- Apply MLOps best practices covering reproducibility, data and model lineage, rollback, monitoring, governance, and operational reliability.
- Partner with ML researchers, robotics engineers, data platform engineers, and other technical stakeholders to translate requirements into scalable infrastructure solutions.
- Integrate ML orchestration and metadata tracking capabilities with existing data lakes, pipelines, and broader platform infrastructure.
- Mentor junior engineers and contribute to architectural decisions, technical standards, and the long-term roadmap for cloud and ML infrastructure.
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