Software Engineering Manager

Who you are

• A demonstrated track record of building, shipping, and operating production software, including hands-on delivery of AI-native application features that real users depend on rather than demos
• Direct experience authoring agentic instructions and prompts, designing AI-driven autonomous workflows, and building the evaluation and testing that verifies them, is required
• Experience delivering conversational experiences in chat and voice,
• Willingness to work directly with customers in a forward deployed capacity is required; prior forward deployed experience is an advantage
• Typically requires a minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree
• 5+ years of experience as a technical lead for technical teams
• Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience
• Advanced degrees or certifications are a plus but are not a substitute for a demonstrated record of shipping reliable AI-native applications
• Software engineering fundamentals. Strong command of data structures, algorithms, system design, APIs, data modeling, and testing
• AI native application development. Hands-on experience building applications where model-driven behavior is central — agent orchestration at the application layer, tool and function calling, context assembly, grounding against enterprise data, and handling latency, cost, and failure
• AI-driven autonomous workflow design. Demonstrated experience designing workflows in which agents carry out multi-step business processes with limited supervision: process decomposition, decision points and autonomy boundaries, human-in-the-loop checkpoints, exception and retry handling, and observability over what the agent did and why
• Conversational and multi-channel design. Practical experience across chat and voice, including Voice AI: turn and context management, intent and entity handling, disambiguation and confirmation patterns, automated-to-human handoff, and the latency and speech recognition constraints voice adds
• Agentic instruction authoring. Demonstrated skill writing and maintaining the natural-language logic that governs agent behavior — instructions, role definitions, tool descriptions, guardrails, and refusal and escalation rules — with versioning, review, and regression coverage applied as they would be to code
• Prompt engineering. Intent-driven prompt design: decomposition, golden and few-shot examples, structured output and schema enforcement, grounding and citation, and disciplined iteration against evaluation results rather than impressions
• Evaluation and testing of non-deterministic systems. Ability to build measurable frameworks assessing response quality, agent behavior, tool-selection accuracy, and regression risk — golden datasets, scenario suites, model-as-judge scoring with human calibration, continuous evaluation pipelines, and drift detection — plus adversarial, jailbreak, and grounding testing
• Specification precision and architectural judgment. Ability to define problems rigorously enough that another engineer or an AI agent implements them correctly, and to decide soundly when to solve a problem in code, in instructions, or by delegating to an agent
• Safety, security, and data handling. Working knowledge of risks specific to AI-integrated applications — prompt injection, sensitive-data and secret leakage, over-broad tool access, unsafe autonomous action — translated into concrete guardrails, least-privilege controls, and monitoring
• Experience in managing cross-functional teams with combined engineering and quality responsibilities
• Voice and contact center technology. Telephony and contact center platforms, IVR, speech recognition and synthesis, streaming audio, and real-time latency optimization
• Conversation design partnership. Experience working alongside conversation or content designers, contributing to dialogue flow, tone, and error-recovery design
• Evaluation and observability tooling. Evaluation frameworks, prompt and instruction management tooling, tracing for LLM applications, and analysis of production transcripts at scale
• Enterprise domain depth. Customer service, contact center operations, sales, or enterprise workflow, at a depth sufficient to challenge a requirement rather than only implement it

What the job involves

• As the Manager of Software Engineering, you will lead the OmniChannel team within Customer Support Management (CSM)
• This hands-on leadership role will set technical direction, driving execution of key initiatives such as Voice AI, Chat AI, Contact Center Integrations among other Omni Channel capabilities
• In addition you will also oversee engineering operations, ensure product quality and scalability, and foster collaboration across global teams
• As a Manager, Software Engineering Management, you will set technical direction and serve as a thought partner to your engineers and product counterparts, staying close enough to the work to make sound technical judgments and unblock your team when it matters
• You will lead a team as a people manager, investing in their growth deliberately, raising the performance bar with intention rather than simply maintaining it, and consistently creating space for innovation alongside a fast-moving technical growth
• Manage product development activities and oversee end-to-end engineering deliverables
• Manage and build a team of engineers by identifying individual strengths, providing career development, and proactively elevating engineers
• Manage daily activities and lead monthly release cycles with product management, providing technical feedback to maintain delivery velocity, ensuring engineering excellence and code quality
• Design conversational experiences across chat and voice. Build experiences that hold context across turns, hand off cleanly between automated and human agents, and behave consistently across channels — accounting for what voice imposes: latency budgets, barge-in, speech recognition error, disambiguation, and confirmation before consequential actions
• Build AI native applications. Design and ship applications built around agentic behavior — intent interpretation, multi-step reasoning, tool invocation, and action on the user's behalf — together with the data models, integrations, and channels that make them usable in production
• Design AI-driven autonomous workflows. Decompose business processes into the steps and decision points an agent can execute — determining where autonomy is appropriate, where a human checkpoint is required, and how exceptions, retries, and hand-back to a person are handled
• Build automated evaluation and test non-deterministic behavior. Design and operate the evaluation that makes change safe — golden datasets, multi-turn conversation suites, model-as-judge scoring calibrated to human review, CI gates, and drift detection — plus adversarial, jailbreak, grounding, and tool-selection testing
• Specify precisely and direct AI coding agents. Convert requirements into testable specifications with explicit scope, constraints, and acceptance criteria; decompose work into agent-sized tasks; and review agent output for correctness and maintainability. You own the result regardless of what produced it
• Own quality, safety, and reliability in production. Monitor conversation quality, containment, hallucination, and unsafe actions; defend against prompt injection and data leakage; and feed production failures back into specifications and evaluation sets
• Collaborate across product, design, and engineering. Partner with product managers, designers, conversation designers, and engineers to define success criteria, align on tradeoffs, and communicate capability and risk clearly
• Solve ambiguous problems and fast-changing priorities by providing clear direction to the team by fostering collaboration and applying structured decision-making

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time