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
Similar to the prior job, this position in NVIDIA offers unmatched market relevance and growth potential in the deep learning sector.
- Cutting-edge opportunities in deep learning at NVIDIA
- Focus on innovation and optimization on a global scale
- Highly competitive compensation and career advancement potential
Pros
- Direct involvement with cutting-edge AI technologies
- Possibility to influence deep learning model deployment
- Attractive salary structures reflecting high-demand skills
Cons
- Requires deep domain expertise, thus narrowing applicant pool
- Potentially high-stress environment due to rapidly evolving technologies
- Limited opportunities for entry-level professionals
Who it's for
Senior • On-site or remote (specific to role)
Good fit
- Senior deep learning engineers
- AI specialists with strong technical backgrounds
- Professionals eager to drive innovation
Not recommended for
- Novices without substantial deep learning experience
- Individuals not interested in technical work
- Those preferring routine tasks without complexity
Motivation fit
Desire for innovation in AI and technology developmentInterest in analyzing and optimizing complex systemsWillingness to adapt to a fast-moving tech landscape
Key skills
Expertise in Python, PyTorch, and deep learning conceptsExperience with inference optimization techniquesStrong skills in analyzing GPU performanceProficient in production deployment strategies
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.
