Job Description

Locations

• Redmond, WA, US

Salary

Skills Required

• coding in languages includingintermediate
• modern AI engineeringintermediate
• internal developer platformsintermediate
• flowintermediate

Required Qualifications

• Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. (experience, 6 years)

Preferred Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, a related technical field, or equivalent practical experience, plus substantial experience delivering complex production software and AI systems. (experience)
• Bachelor’s degree in Computer Science, Engineering, Data Science, a related technical field, or equivalent practical experience, plus substantial experience delivering complex production software and AI systems. (experience)
• Demonstrated success improving execution across engineering teams through operating mechanisms, platform leverage, automation, and measurable constraint removal. (experience)
• Demonstrated success improving execution across engineering teams through operating mechanisms, platform leverage, automation, and measurable constraint removal. (experience)
• Deep hands-on expertise with modern AI engineering, including LLM application patterns, agent orchestration, retrieval, evaluation, data pipelines, and production operations. (experience)
• Deep hands-on expertise with modern AI engineering, including LLM application patterns, agent orchestration, retrieval, evaluation, data pipelines, and production operations. (experience)
• Experience applying AI-assisted or agentic practices across the software-development lifecycle and establishing safeguards that make those practices dependable at scale. (experience)
• Experience applying AI-assisted or agentic practices across the software-development lifecycle and establishing safeguards that make those practices dependable at scale. (experience)
• Strong software-engineering skills in languages such as Python, C#, Java, or C++, with a record of shipping reliable cloud services and developer-facing platforms. (experience)
• Strong software-engineering skills in languages such as Python, C#, Java, or C++, with a record of shipping reliable cloud services and developer-facing platforms. (experience)
• Proven ability to lead through influence, resolve cross-team execution tradeoffs, and communicate clearly with engineers, leaders, customers, and partners. (experience)
• Proven ability to lead through influence, resolve cross-team execution tradeoffs, and communicate clearly with engineers, leaders, customers, and partners. (experience)
• Experience embedding security, privacy, reliability, and responsible-AI principles into architecture, delivery, and engineering practices. (experience)
• Experience embedding security, privacy, reliability, and responsible-AI principles into architecture, delivery, and engineering practices. (experience)
• Experience architecting AI systems on Azure or another hyperscale cloud, including identity, data, compute, networking, observability, and deployment infrastructure. (experience)
• Experience architecting AI systems on Azure or another hyperscale cloud, including identity, data, compute, networking, observability, and deployment infrastructure. (experience)
• Experience building internal developer platforms, coding agents, evaluation systems, or workflow automation that produced measurable improvements in engineering throughput. (experience)
• Experience building internal developer platforms, coding agents, evaluation systems, or workflow automation that produced measurable improvements in engineering throughput. (experience)
• Knowledge of flow and delivery measures such as cycle time, deployment frequency, work-in-progress, change-failure rate, recovery time, and developer experience. (experience)
• Knowledge of flow and delivery measures such as cycle time, deployment frequency, work-in-progress, change-failure rate, recovery time, and developer experience. (experience)
• Experience establishing evaluation programs for nondeterministic systems, including offline benchmarks, red teaming, online experimentation, and human-feedback mechanisms. (experience)
• Experience establishing evaluation programs for nondeterministic systems, including offline benchmarks, red teaming, online experimentation, and human-feedback mechanisms. (experience)
• Track record of creating reusable platforms or standards that materially improved engineering velocity, product quality, or customer outcomes across an organization. (experience)
• Track record of creating reusable platforms or standards that materially improved engineering velocity, product quality, or customer outcomes across an organization. (experience)
• Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year. (experience)
• Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:https://careers.microsoft.com/us/en/us-corporate-pay (experience)
• This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. (experience)

Responsibilities

• Own Execution Excellence and Engineering Throughput
• Translate the team leader’s vision and strategy into clear engineering priorities, executable plans, milestones, decision points, and accountable delivery rhythms.
• Translate the team leader’s vision and strategy into clear engineering priorities, executable plans, milestones, decision points, and accountable delivery rhythms.
• Instrument the end-to-end engineering system to identify constraints in planning, design, implementation, review, evaluation, deployment, and operations; use evidence to improve flow rather than optimizing isolated activities.
• Instrument the end-to-end engineering system to identify constraints in planning, design, implementation, review, evaluation, deployment, and operations; use evidence to improve flow rather than optimizing isolated activities.
• Own measurable improvements in cycle time, deployment frequency, work-in-progress, quality, reliability, and engineer time spent on differentiated work, while avoiding output metrics that reward activity over customer value.
• Own measurable improvements in cycle time, deployment frequency, work-in-progress, quality, reliability, and engineer time spent on differentiated work, while avoiding output metrics that reward activity over customer value.
• Transform Engineering with AI-Native Practices
• Redesign the software-development lifecycle around AI-assisted and agentic workflows for discovery, design, coding, testing, evaluation, security review, documentation, incident response, and service operations.
• Redesign the software-development lifecycle around AI-assisted and agentic workflows for discovery, design, coding, testing, evaluation, security review, documentation, incident response, and service operations.
• Build reusable agents, context systems, evaluation harnesses, and paved paths that allow engineers to move from intent to validated production changes with less friction and stronger safeguards.
• Build reusable agents, context systems, evaluation harnesses, and paved paths that allow engineers to move from intent to validated production changes with less friction and stronger safeguards.
• Establish standards for human oversight, provenance, secure tool use, data boundaries, review depth, and verification so increased velocity does not compromise trust, maintainability, or engineering judgment.
• Establish standards for human oversight, provenance, secure tool use, data boundaries, review depth, and verification so increased velocity does not compromise trust, maintainability, or engineering judgment.
• Lead Hands-On Delivery and Continuous Improvement
• Work alongside engineers on the highest-leverage problems—prototype AI-native workflows, review critical designs and changes, diagnose systemic failures, and remove blockers that impede delivery.
• Work alongside engineers on the highest-leverage problems—prototype AI-native workflows, review critical designs and changes, diagnose systemic failures, and remove blockers that impede delivery.
• Create reusable frameworks, templates, automation, and engineering standards that reduce cognitive load and enable teams to deliver faster without compromising reliability, security, or responsible AI.
• Create reusable frameworks, templates, automation, and engineering standards that reduce cognitive load and enable teams to deliver faster without compromising reliability, security, or responsible AI.
• Run disciplined learning loops through delivery reviews, retrospectives, experiments, and decision records; scale proven practices and retire processes or tools that do not improve outcomes.
• Run disciplined learning loops through delivery reviews, retrospectives, experiments, and decision records; scale proven practices and retire processes or tools that do not improve outcomes.
• Convert Priorities into Customer Outcomes
• Partner with product, customer, and field teams to break strategic priorities into thin, testable increments that produce early evidence and shorten time to customer value.
• Partner with product, customer, and field teams to break strategic priorities into thin, testable increments that produce early evidence and shorten time to customer value.
• Coordinate dependencies and resolve execution tradeoffs across Security, Azure, AI, Data, Research, and Customer Experience while keeping teams aligned to the established vision and strategy.
• Coordinate dependencies and resolve execution tradeoffs across Security, Azure, AI, Data, Research, and Customer Experience while keeping teams aligned to the established vision and strategy.
• Connect delivery measures to customer adoption, task success, security posture, quality, reliability, time-to-value, and responsible-AI performance.
• Connect delivery measures to customer adoption, task success, security posture, quality, reliability, time-to-value, and responsible-AI performance.
• Own Production Trust and Responsible AI
• Establish rigorous evaluation and release criteria for model quality, groundedness, safety, fairness, privacy, security, and abuse resistance.
• Establish rigorous evaluation and release criteria for model quality, groundedness, safety, fairness, privacy, security, and abuse resistance.
• Build telemetry and feedback loops that connect system behavior to customer outcomes, enabling rapid detection, learning, and continuous improvement.
• Build telemetry and feedback loops that connect system behavior to customer outcomes, enabling rapid detection, learning, and continuous improvement.
• Lead technical response to high-severity issues and ensure learnings become systemic improvements in architecture, testing, governance, and operations.
• Lead technical response to high-severity issues and ensure learnings become systemic improvements in architecture, testing, governance, and operations.
• What Success Looks Like
• The team reliably converts vision and strategy into prioritized, executable work with clear ownership, rapid decisions, and predictable delivery.
• The team reliably converts vision and strategy into prioritized, executable work with clear ownership, rapid decisions, and predictable delivery.
• AI-native engineering practices materially reduce cycle time and toil while increasing deployment frequency, evaluation coverage, quality, and production confidence.
• AI-native engineering practices materially reduce cycle time and toil while increasing deployment frequency, evaluation coverage, quality, and production confidence.
• Engineers spend more time on differentiated customer problems because repetitive work, context gathering, verification, documentation, and operational tasks are safely augmented or automated.
• Engineers spend more time on differentiated customer problems because repetitive work, context gathering, verification, documentation, and operational tasks are safely augmented or automated.
• Reusable agents, context store for agents, paved paths, and learning loops spread across the organization, compounding throughput gains without weakening security, responsible AI, or human accountability.
• Reusable agents, context store for agents, paved paths, and learning loops spread across the organization, compounding throughput gains without weakening security, responsible AI, or human accountability.

Benefits

• general: Flexibility: Balance what matters—your work, your life, and your team—through trust, autonomy, and shared accountability
• general: Growth: Stretch your skills, expand your impact, and grow with support that meets you where you are
• general: Wellbeing: Support for your body, mind, and financial future—so you can stay energized and do your best work
• general: Community PCS: Find your people, build your network, and feel supported every step of the way

Travel Requirements

Less than 25%

Tags & Categories