Senior Software Engineer, AI/ML Platform

agilityrobotics; Fremont (CA); Remote; USD 197,000 - 307,000; Full time

Benefits offered by this job

401(k) Plan

Equity

Insurance Coverage

Benefit Start Date

Well-Being Support

Job summary

Agility Robotics is building a scalable ML infrastructure platform to power humanoid AI across perception, controls, and robotics teams. You will architect and implement end-to-end ML workflows, data processing, and model deployment at scale.

You will collaborate with researchers and engineers to define platform requirements, drive reproducibility, and deliver robust, cloud-native infrastructure for production ML workloads.

Qualifications

• 5+ years of software engineering experience with at least 2+ years in ML infrastructure, data platforms or MLOps in production.
• Experience building and maintaining ML platform components (experiment tracking, model registries, training pipelines, 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 (CDK, Terraform).
• Hands-on with processing or modeling multimodal data (sensor logs, camera streams, behavior traces).
• Comfortable collaborating cross-functionally with researchers, data engineers, and robotics teams to ship infrastructure.

Responsibilities

• Design and implement the ML platform for end-to-end data processing, training, evaluation and deployment.
• Develop reliable workflows across cloud compute, Kubernetes, and automation.
• Build core components like model registry, feature store, and experiment tracking tooling.
• Own developer APIs and CLI tools for reproducible ML workflows.
• Implement CI/CD for ML enabling retraining and automated model delivery to production.
• Mentor junior engineers and influence platform roadmap.

Skills

ML Infrastructure

MLOps

Cloud Platforms

Kubernetes

IaC

Data Platforms

Tools

MLflow

WandB

Airflow

Kubeflow

Terraform

Job description

Agility's commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers-tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.

About The Role

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.

Key Responsibilities

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.
• 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

What We're Aiming For (MLOps Level 2)

• 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

Required Qualifications

• 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

Bonus Qualifications

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

Why This Role?

• 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.

Anticipated Base Salary Range

$197,000 - $307,000 USD

In addition to base pay, our competitive total rewards package consists of the following for full-time employees:

• 401(k) Plan: Includes a 6% company match.
• Equity: Company stock options.
• Insurance Coverage: 100% company-paid medical, dental, vision, and short/long-term disability insurance for employees.
• Benefit Start Date: Eligible for benefits on your first day of employment.
• Well-Being Support: Empl