Lead the architectural vision and technical execution for Snowflake’s Streamlit in Snowflake platform, driving performance, scalability, and developer experience across a multi-tenant data ecosystem.
Compensation
The estimated base salary range for this role is $250,000 - $380,000. Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan. The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location. This role is also eligible for a competitive benefits package that includes: medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending & health savings account; at least 12 paid holidays; paid time off; parental leave; employee assistance program; and other company benefits.
Responsibilities
Define and own the architectural vision for the SiS platform spanning the SPCS container runtime, the warehouse runtime, the embedding SDK, developer tooling, and the Snowsight integration layer.
Drive the platform’s evolution toward next-generation capabilities, shaping architectural direction for emerging features.
Serve as the primary technical representative for SiS in company-wide architecture forums.
Establish engineering standards for performance, reliability, and scale ensuring SiS can handle many concurrent users with fast startup times and high availability.
Lead the technical design and review process for high-stakes, cross-functional initiatives across infrastructure, Snowsight, Cortex/AI, security, and open-source Streamlit.
Requirements
Nice to have: open-source Streamlit
Nice to have: similar Python-based app frameworks
AI Infrastructure and Frameworks Intern, Cosmos Lab - 2027
Develop training and post-training systems for advanced Physical AI models, including world foundation models and robot policies using NVIDIA’s GPU infrastructure.
Responsibilities
Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL).
Explore distributed parallelism, sharding, low-precision training, compute–communication overlap, and numerical consistency and efficient weight synchronization between training and inference.
Build Physical AI post-training and RL infrastructure supporting advanced training algorithms.
Connect simulation or, where applicable, real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation.
Optimize these workflows through partitioning, pipelining, data transfer, and synchronization across synchronous, asynchronous, or disaggregated execution.
Internship focused on developing and optimizing AI infrastructure for training, inference, and evaluation of advanced Physical AI models at NVIDIA’s Cosmos Lab.
Responsibilities
Develop and optimize training infrastructure for advanced Physical AI world models
Build Physical AI post‑training and RL infrastructure
Improve efficiency and scalability across training, inference, simulation, and evaluation
Analyze and optimize system performance with researchers
Lead and manage testing activities using Tosca (Tricentis) for automation, including planning, design, execution, defect management, and framework development.
Locations (2)
New Haven, Connecticut, United States
Connecticut, United States
Responsibilities
Lead testing efforts, including test planning, test case design, test execution, and defect management using Tosca.
Work closely with stakeholders to understand project requirements and translate them into test scenarios.
Mentor and provide guidance to the testing team on Tosca tool functionalities and best practices.
Collaborate with development and business teams to resolve issues and ensure smooth testing processes.
Develop automation frameworks and scripts using Tosca for efficient test execution.
Requirements
Nice to have: CI/CD tools such as Jenkins, Azure DevOps, GitLab CI
Nice to have: Defect Management tools like JIRA, Azure Boards
Design and evolve core APIs and developer experience for Modular’s MAX AI framework, focusing on inference and training systems across Python and Mojo.
Locations (3)
Los Altos, California, United States
United States
Canada
Compensation
The estimated base salary range for this role to be performed in the US, regardless of the state, is $180,000–$324,000 USD.
Responsibilities
Influence the design of the API surface for MAX: namespaces, core abstractions, extension points, programming model, compatibility guarantees and developer experience.
Design inference APIs that support real-world serving needs: model loading, distributed inference, quantization, tokenization/pipelines, configuration surfaces, batching/streaming, and deployment-oriented ergonomics.
T
Senior GenAI Engineering Lead — Planning & Execution (Python)
Mandatory Skills Tosca Commander / API Testing
Primary Skills
• 8+ years of hands-on experience in Tricentis Tosca Automation.
• Strong experience in: o Tosca Commander o Test Case Design (TCD) o API Testing o Data-Driven Testing o Distributed Execution (DEX) o Test Data Management
• Experience in enterprise test automation frameworks.
• Extensive experience in Web, API, and ERP application testing.
• Strong understanding of SDLC, STLC, and Agile methodologies Secondary Skills
• CI/CD tools such as Jenkins, Azure DevOps, GitLab CI.
• Defect Management tools like JIRA, Azure Boards.
• SQL and database validation.
• Exposure to SAP, Salesforce, Guidewire, or other enterprise applications.
• Knowledge of performance and integration testing.
This is a remote position.
Design training APIs that scale from single device to distributed execution, with clear primitives for device placement, parallelism, checkpointing, and observability.
Create a coherent programming model across Python and Mojo-adjacent surfaces: align naming, types, and conventions; avoid leaky abstractions; define the "pit of success".
Drive RFCs and technical specs: write and socialize proposals; gather feedback from internal model engineers and external users; iterate towards consensus.
Requirements
Bachelor's degree in Engineering, Information Systems, Computer Engineering, Electrical Engineering, Computer Science, or related field
Master's degree in Engineering, Information Systems, Computer Engineering, Electrical Engineering, Computer Science, or related field
PhD in Engineering, Information Systems, Computer Engineering, Electrical Engineering, Computer Science, or related field
Principal EngineerSnowflake · Bellevue, US
Tosca Automation LeadShrive Technologies LLC · Remote
Senior AI Framework EngineerModular · Los Altos, US+2 more