/ TEAMS · Specialist engineering · CUDA / GPU programming
Source engineers experienced in GPU kernels, parallel computing and performance optimisation. for business-critical production systems, safe integrations and measurable technical ownership.
2,000+ engineers in our active talent network · Regional recruiters
/ SYSTEM STACK
CUDA kernels
memory coalescing
GPU occupancy
streams and events
Nsight profiling
/ AVAILABLE TO HIRE
Profiles show the experience we can source. Individual candidate availability is confirmed after enquiry.
Mid+ CUDA / GPU Engineer
Available5+ years of experience · Slovakia
CUDA / GPU Engineer with 5 years in production systems, focused on CUDA kernels and memory coalescing.
Languages: Slovak, English B2+
Senior CUDA / GPU Engineer
Inquire availability11+ years of experience · Croatia
CUDA / GPU Engineer with 11 years in production systems, focused on memory coalescing and GPU occupancy.
Languages: Croatian, English C1
Senior CUDA / GPU Engineer
Inquire availability14+ years of experience · Croatia
CUDA / GPU Engineer with 14 years in production systems, focused on GPU occupancy and streams and events.
Languages: Croatian, English B2+
Mid-level CUDA / GPU Engineer
Available3+ years of experience · Croatia
CUDA / GPU Engineer with 3 years in production systems, focused on streams and events and Nsight profiling.
Languages: Croatian, English B2+
/ Engineering teams
Onectus uses its active engineering network and regional recruiting partners to search against concrete systems, environments and responsibilities. We evaluate practical experience rather than treating a framework mention or certification as proof of delivery. Particular availability and engagement terms are confirmed after an enquiry. For this role, practical knowledge of CUDA kernels, memory coalescing, GPU occupancy helps define the engineering search.
/ Technical specializations
The right profile depends on your actual architecture, performance constraints, integration boundaries and operational responsibilities.
CUDA / GPU programming responsibilities include cuda kernels, related memory coalescing, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
CUDA / GPU programming responsibilities include memory coalescing, related gpu occupancy, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
CUDA / GPU programming responsibilities include gpu occupancy, related streams and events, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
CUDA / GPU programming responsibilities include streams and events, related nsight profiling, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
CUDA / GPU programming responsibilities include nsight profiling, related multi-gpu transfers, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
CUDA / GPU programming responsibilities include multi-gpu transfers, related cuda kernels, clear test coverage and a reliable production handover. Examine the actual tooling and deployment environment.
/ Project scenarios
Accelerate a numerical compute kernel. Define constraints around cuda kernels, map dependencies and test how the proposed implementation behaves under real operating conditions.
Fix memory-bound GPU workloads. Define constraints around memory coalescing, map dependencies and test how the proposed implementation behaves under real operating conditions.
Reduce host/device transfer costs. Define constraints around gpu occupancy, map dependencies and test how the proposed implementation behaves under real operating conditions.
Profile GPU contention under production load. Define constraints around streams and events, map dependencies and test how the proposed implementation behaves under real operating conditions.
/ Practical evaluation
A senior engineering interview should surface directly relevant implementation details, debugging history and operational judgment.
Describe a production issue involving CUDA kernels and what evidence led to a fix.
Which decisions around memory coalescing and GPU occupancy were you personally accountable for?
Describe a production issue involving GPU occupancy and what evidence led to a fix.
Which decisions around streams and events and Nsight profiling were you personally accountable for?
/ Sourcing process
Define platform versions, deployment constraints, traffic patterns, required integrations and ownership.
Search the active network and partner channels for platform-specific engineering experience.
Discuss real systems, code or configuration decisions, troubleshooting and change control.
Confirm role boundaries, location or remote access, availability, security and interviews.
Profiles, availability and engagement terms are confirmed for each request.
/ Questions
Yes. Tell us the relevant versions, operational environment and the responsibilities connected to CUDA kernels. We qualify hands-on experience during each search.
These can be included when central to the project. We distinguish practical implementation work from general familiarity and ask about failure scenarios and testing.
We look at production decisions, trade-offs, incident diagnosis, code or infrastructure ownership and the ability to explain decisions without relying on a technology checklist.
Share your architecture, technical constraints, collaboration model and priorities. We can then discuss relevant sourcing channels, evaluation criteria and next steps.
/ Onectus · TEAMS
Tell us the technology, production responsibilities, experience level and working model. We'll activate the appropriate engineering sourcing channels.
