Vimaan
Robotics Integration Engineer
Skills
11 days ago
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Important information
Contract type:
Permanent
Location:
San Jose, CA, USA
Work mode:
Onsite
Published on:
13 October 2026
What they need
Team Overview
The Applications Engineering team is responsible for adapting, enhancing, and configuring Vimaan’s robotics and AI solutions to meet the needs of new customers and workflows. The team sits at the intersection of AI/ML, software, hardware, mechanical systems, and field operations—bringing together all engineering functions to deliver reliable, scalable robotics solutions to production environments.
Role Overview
The Applications Engineering Manager (US) leads a cross-functional, customer-facing engineering function responsible for driving solution definition, roadmap contribution, feature requirements, deployment success, and ongoing operational excellence.
You will work directly with customers, internal stakeholders, and global engineering teams to ensure Vimaan solutions deliver measurable impact at scale.
This role requires strong leadership, deep technical understanding, customer empathy, and the ability to execute across ambiguous, multi-disciplinary environments.
Core Responsibilities Customer Engagement & Solution Ownership
- Lead customer discovery, capturing deep insights into workflows, pain points, and operational needs.
- Translate customer requirements into clear product specifications and actionable engineering goals.
- Act as the primary technical point of contact for US customers through deployment, onboarding, and solution scaling.
- Partner closely with enterprise customers to align on success metrics, KPIs, and long-term adoption strategy.
Roadmap & Requirements Leadership
- Define and prioritize functional and technical requirements for new features and workflow improvements.
- Maintain and manage a backlog of enhancements, customer requests, and cross-functional engineering work.
- Collaborate with Product, AI/ML, Software, Hardware, and UX teams to shape the roadmap for new capabilities.
Cross-Functional Program Management
- Own the end-to-end lifecycle of product features: ideation → requirements → development → test → release → iteration.
- Drive alignment across AI/ML, firmware, mechanical, software, and field operations teams to ensure timely delivery.
- Establish and enforce technical validation milestones and quality gates before customer deployments.
- Ensure successful deployment of robotic solutions into live customer facilities.
Systems Integration & Technical Leadership
- Oversee integration of robotics hardware, sensors, ML pipelines, perception systems, software applications, and operator interfaces.
- Guide troubleshooting of complex, multi-layered issues involving hardware/firmware/software/ML interactions.
- Ensure system performance meets customer KPIs, SLAs, and reliability expectations.
Operational Excellence & Reporting
- Create and refine KPIs, dashboards, and analytics that help customers understand solution performance.
- Drive continuous improvement initiatives across deployments, operations, and product workflows.
- Provide executive-level reporting on deployment status, risks, timelines, and customer impact.
Required Qualifications
- Master’s degree in Robotics, CS, Software Engineering, or equivalent experience.
- Strong systems engineering experience, ideally in robotics or complex hardware-software products.
- Hands-on programming experience in Python and scripting experience on Linux systems.
- Proven experience working directly with enterprise customers in a technical capacity.
- Demonstrated ability to lead cross-functional engineering programs from requirements to deployment.
- Strong understanding of ML/AI workflows and how they integrate with robotics systems.
- Exceptional communication, prioritization, and leadership skills.
- Highly detail-oriented with strong ownership and a bias for action.
Preferred Qualifications
- Experience with sensors like LiDAR, RGB-D, IMU, or similar robotics components.
- Knowledge of integration frameworks such as ROS, Git, Jira, and cloud-based deployment architectures.
- Experience with edge computing, GPU workloads, or embedded systems.
- Prior experience scaling early-stage robotics or automation systems into production.
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