Senior Software Engineer, RL Post-Training Frameworks — NVIDIA (ufficio Zurich)
NewCHF 101'500 - 154'000
NVIDIA (ufficio Zurich) · Zürich (ZH)
- Location
- Zürich
- Contract
- full-time
- Posted
- Yesterday
SalaryCHF 101'500 - 154'000
Role overview
Reinforcement learning post-training is driving some of the most significant capability gains in AI today.
It is the process that teaches a model to reason through hard problems, follow complex instructions, and act as an autonomous agent.
It is also one of the hardest infrastructure challenges in the field.
- Reinforcement learning post-training is driving some of the most significant capability gains in AI today.
- It is the process that teaches a model to reason through hard problems, follow complex instructions, and act as an autonomous agent.
Company and context
- Strong verbal and written communication skills and the ability to collaborate across organizational and geographic boundaries
- Depth in one or more of the following technical areas:
- Reinforcement learning for LLM post-training (RLHF, PPO, GRPO, DPO, reward modeling), including how algorithms map to distributed execution and the systems challenges they create (heterogeneous placement, rollouts, environment execution, resharding between training and generation)
- PyTorch internals, including distributed training primitives (FSDP, tensor parallelism, pipeline parallelism) and their composition
- Kubernetes runtime internals (container lifecycle, pod scheduling, resource quotas, GPU allocation)
- End-to-end distributed systems design (service boundaries, data flows, consistency models, failure modes, recovery approaches) Experience in any of the fol
Additional details
- Come join us to build the systems that enable the next generation of AI. What you will be doing:
- It also means advocating for researcher and partner needs with NVIDIA's networking, math library, and compiler teams so the capabilities RL workloads require get prioritized and delivered, and working with hardware teams to take advantage of next-generation hardware capabilities in post-training workloads. What we need to see:
- 5+ years of professional experience in distributed systems, high-performance computing, deep learning infrastructure, or ML systems engineering Strong proficiency in Python and C/C++
- End-to-end distributed systems design (service boundaries, data flows, consistency models, failure modes, recovery approaches) Experience in any of the fol
Notes and original content
- Come join us to build the systems that enable the next generation of AI.
- What you will be doing:
- It also means advocating for researcher and partner needs with NVIDIA's networking, math library, and compiler teams so the capabilities RL workloads require get prioritized and delivered, and working with hardware teams to take advantage of next-generation hardware capabilities in post-training workloads.
- What we need to see:
- 5+ years of professional experience in distributed systems, high-performance computing, deep learning infrastructure, or ML systems engineering
- Strong proficiency in Python and C/C++
- End-to-end distributed systems design (service boundaries, data flows, consistency models, failure modes, recovery approaches)
- Experience in any of the fol
