Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry, turning heavy construction machines into autonomous robots.
Gravis began as an ETH Zurich spin-out, and our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment.
Backed by deep robotics research and now deployed across multiple countries with leading construction and equipment partners, our team is rapidly growing to bring this technology to a trillion-dollar industry.
The Gravis RACK is a machine-agnostic retrofit kit that adds autonomy to excavators and wheel loaders from 10 to 100+ tonnes: LiDAR and camera sensing, GNSS RTK, networking hardware and rugged edge compute that works offline. Paired with the Slate tablet and our Copilot software, it lets an operator run a machine manually, with AI assistance, or fully autonomously.
About the role
At Gravis, the intelligence behind our machines is only as good as the systems that develop, train, and operate it. The Gravis Rack fuses data from LiDAR, cameras, GNSS, and hydraulics into a learning-based control system that adapts in real time to changing ground conditions. As our fleet grows and our models become more sophisticated, we need world-class infrastructure to support the full ML lifecycle: from raw sensor data ingestion on the edge to continuous model training, evaluation, and deployment at scale.
As our MLOps Lead, you will be driving the strategy, technical roadmap, and leadership of our MLOps team. You will serve as both the technical lead and people manager, taking full ownership of building, mentoring, and scaling a high-performing engineering team. The systems you and your team build power every ML experiment, training run, and production deployment at Gravis. You will devise and execute an optimal MLOps vision while collaborating closely with platform and robotics leadership to enable high-velocity and high-quality ML development and deployment across the organization.
What you will do
Own the MLOps technical vision and roadmap, aligning infrastructure investments and architecture decisions with broader company and engineering milestones
Lead and build an engineering team to architect, build, and optimize high-throughput data ingestion pipelines and platform infrastructure for petabyte-scale multimodal datasets (e.g., LiDAR point clouds, camera streams, GNSS/IMU, hydraulics time-series)
Mentor and grow the team members through continuous feedback, career development, and technical guidance
Design, build, and operate high-availability hybrid (cloud and on-premise) compute clusters, providing developers and researchers with a seamless, unified compute experience
Lead the continuous deployment and monitoring pipelines for ML models deployed across thousands of edge devices in the field
Establish full model lifecycle management, incorporating robust model registries, artifact versioning, automated regression testing, and real-time observability/monitoring.
Collaborate closely with robotics engineers to understand requirements and translate them into reliable, scalable training environments
Evaluate and integrate best-in-class MLOps tooling on cloud and on-prem compute platforms
What we're looking for
Bachelor's or Master's degree in Computer Science, Data Engineering, Electrical Engineering, or a related field
7+ years of hands-on experience in ML Ops, data engineering, or ML infrastructure roles, preferably in a leadership role driving the ML Infrastructure
5+ years of production experience with Kubernetes, including designing and maintaining cloud infrastructure (AWS, GCP, or Azure) and on-premise cluster infrastructure
Prior experience in technical leadership or engineering management, including mentoring and growing engineers
Strong background in building, scaling, and optimizing large-scale data pipeline infrastructure for ingesting complex multimodal datasets and high-volume data and organizing them in curated datasets (e.g. using Encord, Scale AI, Lightly )
Proven experience deploying, observing, and maintaining ML models at scale across thousands of edge/remote devices in production.
Deep experience with model registry, artifact versioning, experiment tracking, and data versioning tools (e.g., MLflow, W&B, DVC)
Demonstrated track record of building CI/CD pipelines for ML workloads (e.g., GitHub Actions, GitLab CI) implemented with Infrastructure as Code
Bonus Qualifications
Experience working with robotics data (point clouds, camera streams, timeseries data).
Experience with Robotics & DevOps related tooling (Foxglove, Prometheus, Grafana)
Experience with databases
Demonstrated expertise with large-scale database architectures, indexing frameworks, and high-performance data retrieval
Don't meet every requirement? We still want to hear from you. These are nice-to-haves, not dealbreakers:
This is an opportunity to join a dynamic, multidisciplinary team and to be part of a company that is reshaping heavy construction.
Gravis is an equal opportunity employer. We are committed to building an inclusive and diverse team, and do not discriminate based on race, colour, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics.
Skills Required
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Electrical Engineering, or a related field
- 7+ years of hands-on experience in MLOps, data engineering, or ML infrastructure roles
- 5+ years of production experience with Kubernetes
- Experience designing and maintaining cloud infrastructure using AWS, GCP, or Azure
- Experience designing and maintaining on-premise cluster infrastructure
- Technical leadership or engineering management experience, including mentoring and growing engineers
- Experience building, scaling, and optimizing large-scale data pipelines for complex multimodal and high-volume datasets
- Experience deploying, observing, and maintaining ML models at scale across thousands of edge or remote devices
- Experience with model registries, artifact versioning, experiment tracking, and data versioning tools such as MLflow, W&B, or DVC
- Experience building CI/CD pipelines for ML workloads using tools such as GitHub Actions or GitLab CI with Infrastructure as Code
- Experience with robotics data, including point clouds, camera streams, or time-series data
- Experience with Foxglove, Prometheus, or Grafana
- Experience with databases
- Expertise with large-scale database architectures, indexing frameworks, and high-performance data retrieval
What We Do
Developing autonomy for heavy machinery to automate an industry with a slowly rising productivity and a global labour shortage
