Remote Junior Embedded Software Engineer Jobs in Pennsylvania

Join the team building the machine learning platform to power fleet-scale humanoid robotics. As a senior engineer on the ML Infrastructure and Platform group, you will help architect and build the foundational infrastructure for AI and machine learning operations at Agility. This includes the platform layer for data collection and processing, training, sim and real evaluation, and model management and observability.

Your work will empower our AI teams across perception, controls, skills, and innovation to build and deploy next-generation robot foundation models and end-to-end policies for humanoid robots by providing tools to develop and operationalize machine learning at scale. 

Execution and Technical Ownership

• Contribute to the design and implementation of the ML platform for orchestrating the end to end AI flywheel of 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 tooling.
• Own developer-facing APIs and CLI tools that make ML workflows simple and reproducible. 
• Implement the CI/CD lifecycle for ML that enable continuous retraining, automated testing, and seamless model delivery to production environments

Collaboration

• • Work closely with the Staff ML Infra Engineer and cross-functional stakeholders (AI researchers and robotics engineers) to understand requirements and translate them into scalable solutions/systems.
  • Partner with data platform engineers to integrate ML orchestration and metadata tracking tools with our existing data lake and pipelines.

Engineering Excellence, Growth and Impact: 

• • Apply MLOps best practices: reproducibility, lineage, rollback, monitoring and governance. 
  • Mentor junior engineers and influence the broader cloud platform organization's roadmap.
  • Contribute to internal discussions on platform architecture, reliability, and scalability alongside the broader ML and data platform team

• Version-controlled ML pipelines (data, code, and config)
• Automated and reproducible model training and evaluation
• Continuous integration and delivery for ML workflows
• Centralized experiment tracking and performance visualization
• Standardized model packaging and deployment to production
• Monitoring of models post-deployment

• 5+ years of software engineering experience, with at least 2+ years working on ML infrastructure, data platforms or MLOps systems in production environments.
• Experience building and maintaining components of modern ML platforms—such as experiment tracking, model registries, training pipelines, or deployment systems
• Familiarity with orchestration and tracking tools (MLflow, WandB, Airflow, Kubeflow, etc.)
• Proficiency with cloud-native platforms (AWS, GCP, or Azure), containers, and IaC (e.g., CDK, Terraform)
• Hands-on experience with processing or modeling multimodal data(sensor logs, camera streams, behaviour traces etc).
• Comfortable collaborating cross-functionally with research scientists, data engineers, and robotics/autonomy teams to ship infrastructure used by others

• Experience with robotics, autonomous vehicles, drones or embedded ML.
• Contributions to open-source ML infrastructure or MLOps tooling a plus. 

• Build from the start Join at a pivotal moment - help shape the ML platform layer as its being defined, not inherited. 
• High impact: Your work will directly enable faster, safer, and more intelligent robotic behaviors at scale
• Technical Frontier: You will work directly on enabling the next frontier of AI in real production settings
• Remote-friendly with a strong engineering culture and a fully distributed team.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.