Candidate Profile — Candidate #3
Position: Adobe — Machine Learning Infrastructure Engineer
Candidate Location: San Francisco Bay Area
Experience: 12.8 years experience

Relevant Experience & Education Highlights

Expertise in building large-scale ML training infrastructures, GPU workload simulations for generative models like GANs, and PyTorch-based optimizations positions this candidate strongly for advancing Adobe's Firefly foundation model stack.

1. Build and optimize infrastructures that power large foundation model training on thousands of GPUs

Hands-on experience constructing scalable training systems and performance tools aligns directly with scaling generative AI workloads.

  • Built infrastructure for large-scale training, experimentation, and performance visualizations at a major semiconductor company.
  • Developed Python models simulating deconvolutional neural network architectures in a university research lab.
  • Composed GPU simulations of GAN workloads, comparing against FPGA and CPU platforms to assess resource utilization.
  • Deployed models improving compiler optimization techniques, heuristics, and autotuning processes using PyTorch.

2. Profile GPU utilization, trace inference and training runs and help craft strategies for optimizing our ML model latency

Proven ability to analyze GPU performance and quantify impacts on generative quality and latency supports optimization strategies.

  • Quantified effects of design choices on generative quality, resource utilization, and computation latency in GAN GPU simulations.
  • Leveraged data science pipelines generating actionable insights for partner teams at a major semiconductor company.
  • Studied cutting-edge ML-based code optimization research to deploy performance-enhancing models.

3. Architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust

Background in data pipelines, Docker containerization, and cloud tools like AWS enables robust ML workflow development.

  • Integrated AWS, Docker, HDFS, and PyTorch in building scalable ML experimentation pipelines.
  • Applied exploratory data analysis, neural networks, and probabilistic graphical models across systems.
  • Designed custom software solutions overcoming measurement challenges at a test and measurement company.

Requirements & Candidate Alignment

Adobe RequirementCandidate Qualification
Education: Graduate, PhD, or postgraduate degree in Computer Science, Computer Engineering, or a related field—or equivalent experienceMS and BS from top-tier research and public universities
Experience: 2+ years ML Engineering experience, specializing in generative AI like LLMs12.8 years total experience, including 3+ years ML engineering focused on generative models like GANs and large-scale training
Technical Skills: Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlowAdvanced Python and PyTorch expertise: Built models for neural networks, VAEs, and large-scale training infrastructures
Domain Knowledge: Familiarity with distillation, transformers, and diffusion models. Experience with generative image and video is a plusGenerative AI proficiency: Simulated GAN workloads and deconvolutional networks, matching transformers and diffusion models
Deployment Technologies: Knowledge of deployment technologies such as Docker, ML Ops, and ML servicesDocker and MLOps experience: Deployed scalable ML systems with Docker, Git, and Linux environments
Cloud Platforms: Experience with cloud platforms like Azure and AWS is a plusAWS hands-on: Utilized AWS with Boto3, HDFS for data science and ML pipelines
Problem-Solving: Excellent problem-solving abilities and capacity to analyze complex issues and drive solutions with a data-driven approachData-driven optimization: Analyzed GPU simulations and pipelines to derive insights on performance and heuristics
Communication: Strong verbal and written communication skills and success in cross-functional team environmentsCross-functional collaboration: Advised on solution architectures and generated insights for partner teams

If you would like to discuss this candidate or other critical roles, here is a link to my calendar to schedule a call:

Schedule a Call

Contact:

Jason Rath

TalentPros.AI

Finding the signal in the noise since 2005

512-993-8228

Jason@TalentPros.AI

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