Software Engineering II and Senior Software Engineer (CoreAI - Post Training)

Microsoft•Redmond, WA

6d•$102,100 - $261,000

About The Position

Join CoreAI, where we’re building next-generation systems for large scale reinforcement learning. Our platforms power the full lifecycle of cutting edge LLMs – from rapid experimentation to global production deployment. On our team, you’ll play a pivotal role in making large scale post-training and reinforcement learning workflows faster, safer, and more reliable.

Requirements

Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Python, or Java, OR equivalent experience.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Python, or Java, OR equivalent experience
Experience in cloud software development.
Experience in reinforcement learning, post-training, and container workloads preferred.
Experience with inference technologies such as vLLM, SGLang, and disaggregated inference.
Experience with training and orchestration technologies such as VERL, Slime, and Ray.
Knowledge of containers, GPU computing, and distributed training infrastructure.
Knowledge or experience with the ability to modify, optimize, or improve models and training/inference systems, rather than simply using the technologies.

Responsibilities

Build and evolve distributed services that underpin massive training runs
Improve iteration loops for researchers and engineers
Debug complex interactions between models and hardware
Apply advanced performance optimization techniques that directly impact product quality and operational excellence
Develop deep expertise in ML systems, AI infrastructure, and compute orchestration
Ship platform capabilities that enable mission critical AI workloads for customers around the world
Independently uses appropriate artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC) in a disciplined manner. Takes responsibility for the content of their AI-generated requirements, design documents, code, and other assets, assisting other members of the team to do the same. Uses SDLC and engineering health measures (e.g., Accelerate, SPACE framework, Engineering System Success Playbook [ESSP]) to improve processes and practices, especially those involving AI. Experiments with AI tools and practices to improve their own capabilities.
Leads by example within the team to produce extensible, maintainable, well-tested, secure, and performant code that adheres to design specifications. Continuously improves code performance, testability, maintainability, effectiveness, and cost, while learning about and accounting for relevant trade-offs. Applies metrics to drive code quality and stability. Applies appropriate coding patterns and best practices (e.g., leveraging state-of-the-art generative artificial intelligence [GenAI], approaches to source code organization, naming conventions). Identifies and escalates blockers or unknowns during the development process, communicates how they will impact timelines, and identifies strategies and/or opportunities to address them.
Understands and provides feedback for proposals for architecture, with technical leadership from others. With minimal supervision, tests and explores various design options for a product/solution feature, outlining strengths and weaknesses of each option. Collaborates with architects with minimal supervision to build and modify a product/solution feature, providing feedback as needed. Begins to own or collaborate with other engineers on the architecture of solutions, following technical leadership as applicable. Contributes to the development of design documents that support user stories and other product requirements with oversight. Develops an awareness of the current technology landscape. Escalates and shares findings from investigations with the team and owns some design decisions. Helps to ensure system architecture and individual designs meet performance, scalability, resiliency, cost of goods sold (COGS), and other requirements and expectations. Upholds Microsoft standards of security, privacy, and other compliance requirements and expectations. Understands the importance of building solutions that expand upon the work of others. Contributes to the refinement of product features by escalating findings from analyses to inform decisions regarding the engineering of products.
Understands and applies security best practices and establishes code invariants to model "security as code," ensuring each layer is independently secure, and minimizing risk with minimal supervision. Adopts security standards for clear security code review practices for a set of product features that align with design and engineering principles to raise the security hardening for both protections and detections. Contributes to incorporating deployment gates on security controls, and scanners for a set of product features to prevent regressions and/or vulnerabilities that would have customer impact. Includes required security monitoring to ensure detection of violations with minimal guidance. With minimal supervision, works with relevant security partners to define security promises and security invariants while factoring in attacker/investigator personas for security monitoring and telemetry needs, ensure threat models and premortems validate upstream and downstream assumptions and security invariants, establish security breach drills and security incident response processes (e.g., impact analysis, containment), and ensure that artificial intelligence (AI) safety features are implemented for the AI production systems tied to a set of product features.
Reviews work items to deepen knowledge of product features in partnership with appropriate stakeholders (e.g., technical program managers) and executes project plans, release plans, and work items. Contributes to efforts to break down larger work items into smaller work items and providing estimation. Escalates issues that might cause a delay. Ensures required security protections and detection processes are accounted for in planning with minimal guidance. Contributes to ensuring project plans adhere to security, privacy, and compliance requirements. Ensures all code for a set of product/solution features is properly flighted for quicker mitigation of production incidents with minimal supervision. Calculates capacity for planning, accounting for appropriate failover and backup/restore mechanisms for disaster recovery for a set of features with minimal guidance. Makes considerations for efficient operation of a set of features after it is live with minimal supervision. Contributes to establishing a rollback plan for a set of features.
Maintains operations of live site service, following security best practices when responding quickly to mitigate issues while using the minimum required permissions to do so that arise on a rotational, on-call basis. Implements solutions and mitigations to more complex issues impacting performance or functionality of live site service and escalates appropriately. Reviews and writes incident postmortem and presents insights that drive changes to reduce or eliminate incidents. Independently improves troubleshooting guides (TSGs), wikis, tests, and telemetry to make on-call better, and recommends user-facing support documentation and additional test coverage to reduce likelihood of future user-initiated incidents. Enables secure operations, security monitoring, and integration with live site investigation activities. Identifies and proposes opportunities (e.g., lunch talks, automation, practices, tools) that can be leveraged to improve the live site experience. Adds comprehensive observability and monitoring to services.