Lead Engineer; HPC, GPU, CUDA

Position: Lead Engineer (HPC, GPU, CUDA)
Responsibilities:

- ● We seek an expert to identify architectural changes and/or completely new approaches for
accelerating our deep learning models.

● As an architect you are responsible for converting business needs associated with AI-ML
algorithms into a set of product goals covering workload scenarios, end user expectations,
compute infrastructure and time of execution; this should lead to a plan for making the
algorithms production ready

● Benchmark and optimize the Computer Vision Algorithms for performance and quality KPIs
on the heterogeneous hardware stacks (GPU + CPU, etc.)

● Collaborate with various teams to drive an end to end workflow from data curation and
training to performance optimization and deployment.

Skills Required:

- ● Bachelors or Higher in Computer Science, Electrical Engineering, or related field. A strong
background in deployment of complex deep learning architectures.

● 1+ years of relevant experience in at least a few of the following relevant areas is required in
your work history:
Machine learning (with focus on Deep Neural Networks), including
understanding of DL fundamentals;
Experience adapting and inferencing DNNs for various
tasks;
Experience developing code for one or more of the DNN training frameworks (such as
Torch, Caffe or Tensor Flow):
Numerical analysis, Performance analysis, Model compression
and Optimization & Computer architecture.

● Strong data structures and algorithms knowhow with excellent modern C++ programming skills.

● Good grasp over software engineering and tools like CMake, Make (or Ninja), Clang-Tools, etc.

● Hands-on expertise with Tensor

RT, CuDNN, Py Torch

● Hand-on expertise with GPU computing (CUDA, OpenCL or OpenACC) and HPC (MPI, OpenMP)

● Proficient in Python programming and bash scripting.

● Proficient in Windows, Ubuntu and Centos operating systems.

● Excellent communication and collaboration skills.

● Self-motivated and able to find creative practical solutions to problems.

Good to have:

- ● Hands-on experience with PTX-ISA for CUDA or vector intrinsics like AVX, SSE, etc.

● In-depth understanding of container technologies like Docker, Singularity, Shifter, Charliecloud.

● Hands-on experience with HPC cluster job schedulers such as Kubernetes, SLURM, LSF.

● Familiarity with cloud computing architectures

● Hands-on experience with Software Defined Networking and HPC cluster networking.

● Working knowledge of cluster configuration management tools such as Ansible, Puppet, Salt.

● Understanding of fast, distributed storage systems and Linux file systems for HPC workloads.