Senior Deep Learning Engineer
Full Time
full time
9 Oct 2025
Poland, Warsaw
Verified by Turrior
Content + Source + Freshness • 12 Dec 2025 • 95% confidence
90 / 100
Offer value
This role stands out due to its focus on cutting-edge technology in deep learning, high compensation potential, and the strong brand of NVIDIA.
- Advanced role in one of the most sought-after technology companies
- Focus on impactful innovations in deep learning
- High earning potential and professional development opportunities
Pros
- Working with NVIDIA, a leader in AI and deep learning
- Highly competitive salary and open to significant innovations
- Opportunity for impactful contributions in GPU optimization
Cons
- Requires highly specialized skill sets, limiting candidate pool
- Intense focus on performance may create high-pressure situations
- Limited flexibility due to product development timelines
Who it's for
Senior • On-site or remote (specific to role)
Good fit
- Experienced deep learning practitioners
- Engineers motivated by AI advancements
- Candidates interested in optimizing GPU technology
Not recommended for
- New graduates without deep learning experience
- Those uninterested in hands-on technical work
- Individuals preferring simple development roles
Motivation fit
Desire to innovate in AI and deep learningInterest in performance optimizationWillingness to engage in a high-paced research-driven environment
Key skills
Deep learning conceptsProficiency in Python and PyTorchExperience with inference frameworksGPU performance tuning
Score: 90/100 AI verified analysis
About the job
Requirements:
- 5+ years of experience.
- MSc or PhD in CS, EE, or CSEE or equivalent experience.
- Strong background in Deep Learning.
- Strong programming skills in Python and PyTorch.
- Experience with inference optimization techniques (such as quantization) and inference optimization frameworks, one of: TensorRT, TensorRT-LLM, vLLM, SGLang.
Nice to haves:
- Familiarity with deploying Deep Learning models in production settings (e.g., Docker, Triton Inference Server).
- CUDA programming experience.
- Familiarity with diffusion models.
- Proven experience in analyzing, modeling, and tuning the performance of GPU workloads, both inference and training.
What you'll be doing:
- Improve inference speed for Cosmos WFMs on GPU platforms.
- Effectively carry out the production deployment of Cosmos WFMs.
- Profile and analyze deep learning workloads to identify and remove bottlenecks.
