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 TensorFlow): 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 TensorRT, CuDNN, PyTorch

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.