Senior Research Scientist​/Engineer - AI Infrastructure Technology - Algorithm San Jose Regular

Location:

San Jose

Team:
Algorithm

Employment Type:

Regular

Job Code: A59236

We are seeking a highly skilled and motivated AI Infrastructure Engineer to join our dynamic team. In this role, you will be responsible for designing, building, deploying, and maintaining the robust and scalable infrastructure that powers our cutting-edge artificial intelligence (AI) and machine learning (ML) initiatives. You will work closely with our AI/ML researchers, data scientists, and software engineers to create an efficient, high-performance environment for training, inference, and data processing.

Your expertise will be critical in enabling the next generation of AI-driven products and services.

• Lead end-to-end design of scalable, reliable AI infrastructure (AI accelerators, compute clusters, storage, networking) for training and serving large ML workloads.
• Define and implement service-oriented, containerized architectures (Kubernetes, VM frameworks, unikernels) optimized for ML performance and security.
• Profile and optimize every layer of the ML stack—ML Compiler, GPU/TPU scheduling, NCCL/RDMA networking, data preprocessing, and training/inference frameworks.
• Develop low-overhead telemetry and benchmarking frameworks to identify and eliminate bottlenecks in distributed training and serving.
• Build and operate large-scale deployment and orchestration systems that auto-scale across multiple data centers (on-premises and cloud).
• Champion fault-tolerance, high availability, and cost-efficiency through smart resource management and workload placement.
• Architect and implement robust ETL and data ingestion pipelines (Spark/Beam/Dask/Flume) tailored for petabyte-scale ML datasets.
• Integrate experiment management and workflow orchestration tools (Airflow, Kubeflow, Metaflow) to streamline research-to-production.
• Partner with ML researchers to translate prototype requirements into production-grade systems.
• Mentor and coach engineers on best practices in performance tuning, systems design, and reliability engineering.

Required Qualifications:

• Master's degree (PhD’s degree is preferred) in Computer Science, Engineering, or a related technical field.
• 5+ years in infrastructure or systems engineering focused roles, with at least 2 years focused on ML/AI infrastructure.
• Strong programming skills in Python, C++, Go, or Rust for systems development and automation.
• Ability to design end-to-end systems that balance performance, reliability, security, and cost.
• Excellent communicator able to bridge research and production teams.
• Strong problem-solving aptitude and a drive to push the state of the art in ML infrastructure.

Preferred Qualifications:

• Hands‑on experience with ML training frameworks (PyTorch, Tensor Flow, JAX) at scale.
• Knowledge of hardware‑level optimization: CUDA, ROCm, kernel bypass, FPGA/ASIC integration.
• Experience with heterogeneous computing for AI, Bigdata, HPC.
• Open‑source contributions or patents in the ML systems space.
• Publications in ML or System Conferences such as MLSys, ICML, ICLR, KDD, NeurIPS.

The base salary range for this position in the selected city is $187,040 - $359,720 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short‑term and long‑term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.