we are seeking a strong ML Software Engineer to join our deep learning LiDAR & Radar group and help scale the systems that bring cutting‑edge perception models into production. Youll build the software layers, data pipelines, and runtime systems that turn advanced neural networks into reliable, high-performance solutions running on edge devices.
This is a hands-on, high‑ownership role within a growing group working closely with algorithm developers. The work spans Python and C++, ML infrastructure, model integration, performance optimization, and production delivery.
What will your job look like:
Lead end-to-end development of features – from design and implementation to integration, testing, and deployment
Build ML pipelines for data-based diverse dataset creation and efficient model inference
Design data selection and sampling strategies to ensure coverage of rare and critical scenarios
Partner with algorithm teams to translate model weaknesses into data curation criteria
Develop validation and diagnostics to measure dataset quality-not just pipeline health but training effectiveness
Integrate neural network models into C++ production systems, including runtime, data flow, and pre/post‑processing
Bring models from research/prototype stage into robust, production‑ready deployments
Optimize runtime performance (latency, memory, and throughput) in resource‑constrained environments
Contribute to deployment flows (e.g., model conversion, profiling, optimization)
Build and improve CI/CD pipelines, automated testing, and development workflows.
This is a hands-on, high‑ownership role within a growing group working closely with algorithm developers. The work spans Python and C++, ML infrastructure, model integration, performance optimization, and production delivery.
What will your job look like:
Lead end-to-end development of features – from design and implementation to integration, testing, and deployment
Build ML pipelines for data-based diverse dataset creation and efficient model inference
Design data selection and sampling strategies to ensure coverage of rare and critical scenarios
Partner with algorithm teams to translate model weaknesses into data curation criteria
Develop validation and diagnostics to measure dataset quality-not just pipeline health but training effectiveness
Integrate neural network models into C++ production systems, including runtime, data flow, and pre/post‑processing
Bring models from research/prototype stage into robust, production‑ready deployments
Optimize runtime performance (latency, memory, and throughput) in resource‑constrained environments
Contribute to deployment flows (e.g., model conversion, profiling, optimization)
Build and improve CI/CD pipelines, automated testing, and development workflows.
Requirements:
B.Sc. in Computer Science, Software Engineering, or equivalent
3+ years of hands-on C++ development experience
3+ years of hands-on Python development experience, including the PyData stack (NumPy, Pandas)
Experience working in Linux environments
Strong motivation to work closely with deep learning algorithms and production of AI systems
Interest in neural network deployment on edge devices, including inference runtimes, performance optimization, and model integration
Proven ability to work across team boundaries (algorithms, infra, product)
Strong motivation to work on production AI systems and deep learning integration
Interest in edge deployment, inference runtimes, and performance optimization
Advantages:
Experience with autonomous-driving datasets or perception pipelines
Background in 3D geometry and/or strong mathematical foundation
Experience with workflow orchestration tools (Airflow, Argo)
Familiarity with data curation techniques (e.g., active learning, hard example mining, distribution balancing)
2+ years in data engineering or backend systems with large‑scale data (production environments).
B.Sc. in Computer Science, Software Engineering, or equivalent
3+ years of hands-on C++ development experience
3+ years of hands-on Python development experience, including the PyData stack (NumPy, Pandas)
Experience working in Linux environments
Strong motivation to work closely with deep learning algorithms and production of AI systems
Interest in neural network deployment on edge devices, including inference runtimes, performance optimization, and model integration
Proven ability to work across team boundaries (algorithms, infra, product)
Strong motivation to work on production AI systems and deep learning integration
Interest in edge deployment, inference runtimes, and performance optimization
Advantages:
Experience with autonomous-driving datasets or perception pipelines
Background in 3D geometry and/or strong mathematical foundation
Experience with workflow orchestration tools (Airflow, Argo)
Familiarity with data curation techniques (e.g., active learning, hard example mining, distribution balancing)
2+ years in data engineering or backend systems with large‑scale data (production environments).
This position is open to all candidates.
