Engineering Manager

• Own the UX and growth of the non-biology aspects of Plasmidsaurus's web app
• Talk to researchers about accelerating their science and distill customer feedback into a roadmap with engineering teams
• Create user experiences that set a benchmark for biotech customers
• Own onboarding, collaboration, payments, order and logistics tracking, account settings, emails, internationalization, workspaces, and sharing
• Improve analytics and visibility into key customer workflows
• Identify drop-off points in onboarding, ordering, and sharing results
• Run experiments to improve customer workflows

Product Manager – Software

• Lead the Dashboard engineering team across customer onboarding and growth, billing and monetization, and AI assistance
• Build and lead a high-velocity team that ships polished product quickly
• Read pull requests, write prototypes, and work with technical leads on architecture and project scope
• Shape the roadmap with product managers, design, go-to-market, finance, and engineering
• Identify customer problems, propose directions, and drive product decisions from problem definition through outcome measurement
• Improve the customer journey from signup to production and expansion using product signals and customer feedback
• Balance rapid product iteration with billing reliability and correctness and high-quality AI assistance
• Drive clarity, focus, urgency, and sustainable execution
• Spend time with customers and engineers to investigate pain points, test assumptions, and improve engineering craft
• Coach, mentor, and grow engineers through feedback, goals, and ownership
• Partner with recruiting to attract, hire, and retain exceptional engineering talent
• Build growth and onboarding capabilities for developers
• Develop annual-credit, automated billing, pricing, usage, and credit visibility features
• Coordinate Dashboard Agent, WorkOS MCP server, Ask WorkOS, and Support Bot experiences with shared retrieval, evaluations, and quality standards

Engineering Manager – Dashboard

• Manage and grow a team of engineers in San Francisco
• Lead engineering teams building ingestion, transformation, and lineage infrastructure for traceable financial data
• Own delivery of flexible financial reporting, composition, and drill-down experiences
• Guide development of accounting automation that structures messy financial data and automates manual work
• Lead development of complex accounting workflows with dependency management, automation, AI-native experiences, and user-focused product design
• Develop people, build trust on teams, and make decisions about people and priorities
• Stay involved in system design, code reviews, and architecture decisions
• Help build the engineering management function, including processes, rituals, and standards
• Protect and amplify high-quality individual-contributor work and ensure teams solve user problems

• Own the direction, delivery, and quality of a core product area from idea through launch, working directly with the founders
• Hire and grow a small team of strong engineers
• Design, write, and review code across the stack, spending roughly one-third to one-half of the time hands-on
• Turn ambiguous product goals into executable technical plans
• Collaborate with hardware, software, and design teams on products deployed to an IoT fleet of physical kiosks
• Decide what to rebuild for scale and what to leave unchanged
• Carry production ownership and participate in on-call responsibilities
• Mentor engineers on the team and across the wider engineering group
• Own integrations enabling kiosks to operate as point-of-sale devices, including kiosk software, backend services, and customer-facing features
• Set technical direction and remain accountable for work quality and team member growth

Software Engineering Manager

• Own the vision, strategy, and execution of Glacier's computer vision roadmap in partnership with Product Management
• Lead, mentor, and develop the distributed CV engineering team, including hiring, performance, upskilling, and retention
• Set project- and task-level priorities, keeping engineers focused and unblocked
• Break complex technical challenges into clear plans and coordinate execution across multiple engineers and workstreams
• Provide technical guidance on models, datasets, compute, evaluation, and production performance
• Establish lightweight processes that improve execution and collaboration
• Partner with Software, Operations, Manufacturing, and Field Engineering to identify and resolve cross-functional dependencies
• Oversee the labeling function, including resourcing, budget, and quality
• Shape computer vision strategy, team execution, and cross-functional collaboration as Glacier scales
• Lead technical direction without serving as a day-to-day developer

Engineering Manager, Computer Vision

• Manage a team of multiline and/or property field engineers and ensure work meets quality control standards.
• Ensure production goals and report turnaround times meet or exceed established goals.
• Complete joint field visits with field engineers to provide quality control and guidance.
• Manage subcontractor selection, assignments, due dates, and report quality.
• Establish and monitor risk improvement service plans for large accounts focused on key loss sources.
• Secure specialty service quotes for Industrial Hygiene, large chemical property risks, and Ergonomics.
• Collaborate with management and the Director of Risk Engineering.
• Participate in monthly Underwriting meetings and provide recommendation compliance reports.
• Ensure consistent service across all sites of multi-location accounts.
• Promote effective use of loss data systems and Risk Engineering services to producers and prospective accounts.
• Supervise staff of 5, including interviewing, hiring, training, work assignment, performance appraisal, rewards, discipline, and problem resolution.

Risk Engineering Manager

• Lead the AI/ML engineering team building payment-routing optimization, authorization-rate improvement, and AI-powered digital workforce systems
• Manage approximately 7–8 ML, backend, and platform engineers
• Guide architecture decisions and unblock the team while remaining technically engaged
• Own team delivery, including scoping, costing, and sequencing initiatives
• Hire, develop, and retain engineers; manage performance and career growth
• Coach engineers through technical designs, code reviews, architecture reviews, and trade-offs
• Set standards for code quality, testing, and CI/CD
• Guide ML model lifecycle architecture, including training, evaluation, monitoring, and retraining
• Guide LLM workflows, agent orchestration, RAG pipelines, and vector database integrations
• Oversee inference services for live payment routing and ensure latency and reliability
• Ensure AWS infrastructure, CI/CD, observability, dashboards, tracing, and on-call runbooks meet high standards
• Apply PCI-DSS and data-handling judgment to payment-data systems
• Translate product vision into a technical roadmap
• Collaborate with product, operations, and modeling leadership
• Report team progress and blockers to leadership

Engineering Manager – AI

• Lead and grow a global team of engineers developing tools to deploy, observe, and maintain a highly available cloud platform at massive scale
• Manage and mentor a team of 3–6 full stack engineers
• Provide technical guidance and code review oversight
• Lead a remote-first team building APIs and UIs in Go and TypeScript
• Collaborate with senior leadership to execute roadmaps and priorities
• Ensure delivery of high-quality software solutions on schedule
• Support team culture and individual professional development
• Participate in hiring and onboarding new team members
• Contribute to technical decisions and architectural discussions
• Build and operate CrowdStrike's internal cloud management platform

Engineering Manager, Infrastructure

• Manage and grow a team of 6 to 8 engineers
• Handle hiring, performance management, and career growth
• Develop engineers who can independently manage customer escalations, data migrations, and incidents
• Set the roadmap with the product partner
• Transition customers from requested reports to self-service reporting and independent book closing
• Own financial correctness across the platform
• Maintain shadow parity harnesses, cross-report tie-out checks, drift monitoring, and correctness SLOs
• Lead multi-quarter ledger report and OLAP golden-view migrations with parity checks
• Maintain customer relationships with comptrollers, CFOs, and RCM directors during month-end
• Translate customer definitions of correctness into software
• Convert recurring escalations into roadmap priorities
• Coordinate execution with payments/posting, data infrastructure, and operations teams
• Ensure upstream changes do not break downstream financial numbers

Senior Engineering Manager, Financial Data, Reporting & Analytics

• Hire and develop engineers, coach technical leaders, provide feedback, and establish ownership and accountability
• Set technical direction and turn the product roadmap into focused, reliable releases
• Review architecture and code, investigate difficult failures, and guide consequential technical decisions
• Translate scientific and customer needs into scoped milestones
• Make tradeoffs, resolve dependencies, and maintain focus as product and research agendas evolve
• Guide architecture across scientific interfaces, agent and tool orchestration, local and remote compute, durable execution, and persistent project state
• Build shared components supporting new scientific workflows
• Preserve analysis inputs, versions, results, and decisions to make scientific work inspectable and reproducible
• Establish testing, release practices, observability, and an effective on-call process
• Improve workflow reliability, latency, and cost; lead resolution of production problems
• Coordinate with Codex, product, design, research, customer-facing engineers, security, privacy, and safety teams
• Lead decisions about building or integrating scientific tools, specialist models, data sources, and experimental service providers
• Work with scientists and design partners to understand workflows and turn customer learning into reusable improvements
• Address enterprise requirements including private compute, credentials, networking, permissions, and data handling
• Connect internal research capabilities with supported product features
• Build instrumentation and feedback pipelines to measure successful workflows and repeat use
• Support evaluation and model improvement under consent and data-use requirements
• Implement access controls, review points, auditability, and retention policies throughout scientific workflows