Jobs at Avride

Design, build, and operate Python backend services, APIs, databases, and labeling pipelines for massive autonomous vehicle sensor datasets. Own systems from ingestion through delivery, integrate vendors and internal tools, develop validation and quality monitoring, improve human labeling with ML automation, and collaborate with ML and perception teams. Lead technical design, review code, mentor engineers, and support production reliability through deployment, monitoring, and on-call responsibilities.

Lead development of Avride’s ML infrastructure, initially owning GPU inference performance for onboard and simulation workloads. Build and optimize C++ execution frameworks, runtimes, and distributed infrastructure supporting neural network pipelines. Collaborate with applied ML teams on model architecture, diagnose performance issues, and expand ownership across broader ML pipelines. The role requires deep GPU and PyTorch knowledge, distributed systems experience, and strong expertise in multithreaded C++ environments.

The QA Engineer will design and execute testing plans for autonomous driving software, analyze test scenarios, and document outcomes. Collaboration with cross-functional teams is crucial for improving test processes and safety metrics.

Own autonomous-vehicle safety testing across QA gates, acceptance criteria, reporting, release decisions, and evidence generation. Define and calibrate safety thresholds, classify findings, connect hazard analyses to scenario coverage, coach QA engineers, validate metrics, translate safety frameworks into testable requirements, and track applicable regulations. The role requires influencing cross-functional teams, documenting residual risk clearly, and supporting safety cases and regulatory readiness.

Owns quality acceptance and release qualification for analytical metrics used in autonomous-driving software decisions. Responsibilities include defining comprehensive case spaces, identifying silent failures, reviewing implementations, creating test data, building regression tests, quantifying defects, and communicating evidence-based fixes. The role requires strong Python and SQL, statistical literacy, product judgment, and experience testing data-heavy systems. Autonomous vehicles, robotics, ML evaluation, ClickHouse, A/B testing, and annotation experience are preferred.

Develop and maintain CI/CD pipelines, hardware-in-the-loop systems, firmware deployment tools, diagnostics, and embedded-system testing infrastructure. Collaborate with hardware and software teams on system architecture, monitor infrastructure performance, resolve firmware development issues, and improve build, test, and deployment workflows for autonomous vehicles and robots.

The Machine Learning Engineer will develop and optimize models, manage datasets, enhance training pipelines, and collaborate across teams to innovate in autonomous vehicle technology.

Develop and maintain the software framework for autonomous vehicle autopilot systems, focusing on performance, safety, and reliability, while implementing automated testing processes.

Develops motion planning algorithms, collaborates with engineering teams, and optimizes performance for autonomous vehicle decision-making.

As a Senior Frontend Engineer, you will develop low-latency interfaces for autnomous vehicle control, optimizing for performance and integrating real-time data.

As a Machine Learning Engineer, you will design, train, and deploy ML models for motion planning, develop data pipelines, and integrate models for real-time use in autonomous vehicles.

Lead development of Avride's control system by architecting and shipping Model Predictive Control (MPC) solutions in modern C++, identifying vehicle dynamics parameters from real-world data, profiling for real-time performance, and providing technical leadership, code review, and mentorship to a small controls team while aligning with Planning and Hardware.

Analyzes autonomous-vehicle data and driving scenarios to validate dataset quality, identify edge cases and coverage gaps, monitor quality metrics, and investigate anomalies. Reviews annotation batches, maintains dashboards and reports, and automates validation and testing workflows using Python, Pandas, and ClickHouse. Collaborates with product and engineering teams to improve annotation efficiency, testing processes, and data quality.

Build and architect React/TypeScript web applications powering internal engineering tools (simulation, fleet tracking). Collaborate with simulation, analytics, and ops teams to integrate systems, shape APIs, and deliver performant, reliable UIs that enable diagnostics, scenario mining, and fleet configuration.

Own and architect the top-level safety case for autonomous vehicles, decompose claims and required evidence against UL 4600 and aligned standards, coordinate cross-pillar evidence production, maintain traceability tooling, prepare for third-party assessments, and report safety case maturity and gate readiness to leadership.

The Senior Brand Designer will develop and maintain Avride's visual identity, design marketing materials, uphold design standards, and collaborate on campaign concepts. They will influence visual communication for a tech-focused brand.

Design, develop, and maintain low-latency device drivers and sensor pipelines for cameras, LiDAR, radar, IMU, GNSS and other sensors. Implement timestamping, filtering, preprocessing, and sensor-health analysis. Collaborate with embedded, perception, and localization teams to integrate and debug hardware/software, optimize performance, and act as the sensor-domain expert guiding solutions and troubleshooting.

Develop and implement on-board localization and cloud mapping algorithms, optimizing rover localization accuracy and cloud mapping processes in challenging environments.

The ML Platform Engineer will build scalable architecture for ML training workloads, optimize system performance, and collaborate with teams for enhanced efficiency on Kubernetes.

Own and harden on-board networking and Linux platform for autonomous vehicles: design fault-tolerant internal networks, implement PTP/gPTP time synchronization, tune Linux networking (NIC buffers, offloads, NAPI), build connectivity daemons (Go/Python) for multi-carrier cellular bonding, integrate networking with embedded Linux on x86_64 and ARM64, and build observability for fleet connectivity.