Senior AI Engineer, Zoom
$209-275.4k
+ Equity + Bonus
More information about location
Office located in San Jose, CA
Work platform for human connection
Open for applications
Work platform for human connection
1001+ employees
Open for applications
$209-275.4k
+ Equity + Bonus
More information about location
Office located in San Jose, CA
1001+ employees
Company mission
To help people connect, collaborate, and get more work done, together.
Role
Who you are
- Requires a Bachelor's degree in Communications Engineering, Artificial Intelligence, Software Engineering, a related field, or a foreign degree equivalent.
- Must have 2 years of experience in job offered or related occupation
- Designing, Implementing, or optimizing large-scale distributed training systems using technologies like Horovod, DeepSpeed, PyTorch Distributed, or Ray
- Tensor/model parallelism and pipeline parallelism
- Utilizing cloud-native or on-prem infrastructure (Kubernetes, Docker, Slurm) to support scalable, fault-tolerant, and resource-efficient AI workloads across multi-node GPU clusters
- Using Performance Profiling and Optimization to diagnose and improve end-to-end training performance by optimizing data pipelines (e.g., DALI, tf.data), minimizing communication overhead (e.g., NCCL, gRPC), and tuning hardware-specific kernels (e.g., CUDA, Triton)
- Systems Programming and Automation in systems-level programming with Python, Bash, and C++ or Go
- Automating deployment and orchestration of AI workloads and monitoring using Prometheus, Grafana, Weights & Biases
- Telecommuting work arrangement permitted one day a week. Four days in office required. Position does not require domestic or international travel
What the job involves
- Develop the Machine Learning Platform management system
- Design and implement intuitive user interfaces and APls for seamless interaction with the platform
- Ensure robust access control and security measures for the Machine Learning Platform
- Regularly evaluate and enhance platform performance, scalability, and reliability. Integrate tools for data versioning, experiment tracking, and workflow orchestration
- Build the toolchains, service, pipeline for model development workflow, and model serving architecture
- Create automated pipelines for data preprocessing, feature engineering, and dataset versioning
- Develop Cl/CD pipelines for deploying models into production environments with minimal downtime
- Enable support for distributed model training and hyperparameter optimization
- Incorporate A/B testing frameworks for evaluating multiple model deployments
- Collaborate with data scientists and engineers to streamline the model development lifecycle
- Prioritize various metrics for model training and inferencing monitoring. Implement logging and monitoring tools to track model performance, resource utilization, and throughput
- Develop dashboards to visualize key metrics such as latency, accuracy, and drift detection in realtime
- Establish alerting mechanisms to detect and respond to anomalies or performance degradation
- Continuously refine metric prioritization based on stakeholder feedback and evolving business goals
- Develop and maintaining the high-performance LLM training GPU infrastructure and cluster
- Optimize GPU utilization for large-scale training workloads, ensuring minimal resource wastage
- Implement fault-tolerant and distributed training strategies for handling large language models (LLMs)
- Evaluate and integrate emerging hardware technologies, such as TPUs, into the training infrastructure
- Regularly update cluster configurations to support new frameworks and model architectures
- Manage scheduling and resource allocation for multi-tenant GPU clusters
- Understand the auto scale for inference service and multi-models for dynamical loading
- Design systems that dynamically allocate resources based on real-time demand for inference services
- Develop mechanisms for loading and unloading models in memory to optimize latency and resource usage
- Implement strategies for caching frequently used models to improve inference performance
- Experiment with serverless architectures to further enhance scalability and cost efficiency
- Ensure compatibility with edge devices and deploy lightweight models for edge inference
- Support, troubleshoot, and resolve any issues during the training and inferencing
- Create detailed runbooks for common troubleshooting scenarios to reduce resolution times
- Perform root cause analysis for failures and implement long-term fixes to prevent recurrence
- Collaborate with DevOps and IT teams to ensure the stability of underlying infrastructure
- Develop self-healing systems that can automatically recover from common training or inference issues
- Provide technical support and guidance to data scientists and engineers working on the platform
Application process
- End Date: June 4, 2026
Our take
Zoom reworked video conferencing when most people assumed it was a solved problem, with services from Cisco and Skype already ubiquitous in workplaces and homes. However, its videoconferencing infrastructure was simpler to use, faster and more flexible than any previous solution, and it swiftly took the world by storm.
Its success bred competition, however, and companies such as Microsoft and Google, along with numerous smaller communications firms, have begun making inroads on Zoom's formerly commanding lead. Additionally, as workplaces are mostly adopting a hybrid or flexible working strategy, some of its importance as a workplace tool is wearing off and it is recording a gradual decline in daily usage rates.
To counter these trends, Zoom is expanding its business offerings through product development and strategic acquisitions. Alongside many other large tech companies, it has battled through a difficult economic situation, but it nonetheless remains a profitable company and household name. Plus, its track record of innovation means it is still very much one to watch in the communications space.
Freddie
Company Specialist at Welcome to the Jungle
Insights
7% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Jan 2017
$100m
SERIES D
Feb 2015
$30m
SERIES C
Total funding: $148.5m
Company benefits
- Work from home opportunities
- Retirement benefits
- My-Time-Off (MTO), an extended vacation policy
- Employee Stock Purchase Plan (ESPP) and Restricted Stock Unit (RSU) Equity Program plus Yearly stock grant for all employees
- Wellness and fitness reimbursement, wellness resources, Thrive Global wellness app, TaskHuman wellness coaching app
- Employee Assistance Program (EAP) including emotional support, financial resources and legal guidance
- Paid Parental Leave (both parents) 16 weeks paid leave (or statutory if more generous)
- Employer Matching of Charitable contributions up to $500 USD equivalent/year
- Annual Zoom Store Allowance
- Business Travel Accident Insurance
Company HQ
Downtown San Jose, San Jose, CA
Leadership
Eric Yuan
(CEO & Founder)Previously VP Engineering at Cisco and WebEx.
Salary benchmarks
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