Senior Software Engineer

Whisker•Auburn Hills, MI

3h•Onsite

About The Position

Whisker is redefining what it means to live with cats—designing intelligent systems that remove friction, elevate the everyday, and celebrate the quiet brilliance of feline companionship. Today, Litter-Robot leads the category. Tomorrow, an entire ecosystem that expands what’s possible for cats and the people who love them. We believe the future is feline. And we’re imagining that future today. We work onsite 4+ days a week, with our team based in Auburn Hills, Michigan, and Juneau, Wisconsin. Our team of 700+ passionate pet people thrives on collaboration, innovation, and the occasional office cameo from a four-legged friend. What You’ll Do: The Senior Software Engineer, Platform builds and operates the shared services, pipelines, and infrastructure that Whisker's product engineering teams depend on. This is a hands-on senior individual contributor role on a newly formed Platform team: you will design and operate backend services at scale, lead migrations of production systems that serve a live fleet of connected devices, and build the ingestion path that moves high-volume IoT telemetry into Whisker's analytics platform. You will own the reliability of what you build, including on-call, and you will raise the technical bar of the engineers around you through design review, mentorship, and the standards you set in code. We build with AI agents here, deliberately and with guardrails, and we are extending that capability into the platform itself. You will be expected to work fluently with agentic tooling — and to hold the line that the engineer who opens the pull request is accountable for it, whether or not a model wrote the first draft. This role suits an engineer already operating at the top of Senior who wants a wider platform charter than a single product team can offer. Summary: The Senior Software Engineer (Platform) at Whisker is responsible for designing, building, and operating the shared backend services, infrastructure, and high-volume IoT telemetry pipelines that support the company's product engineering teams. This senior-level individual contributor role requires hands-on expertise in cloud-native AWS environments and agentic development workflows, with a core focus on system reliability, platform migration, and technical leadership.

Requirements

BS in Computer Science, Computer Engineering, or equivalent experience
7+ years of backend development with 5+ years architecting systems in AWS
Strong proficiency in Python and/or TypeScript, and in SQL
6+ years of experience in backend development
Hands-on experience with core AWS services relevant to this role, such as Lambda, API Gateway, DynamoDB, Kinesis, ECS, and IAM.
Current, hands-on practice with agentic development tooling, and the judgment to go with it: working an agent from structured context rather than one-line prompts, breaking work down so each step can be reviewed, correcting an approach before it becomes two hundred lines, and running an agent-assisted review pass before a human sees the pull request. We care about how you work, not which vendor's tool you have used.
Accountability for AI-assisted output. You can explain every line you submit and defend it in review; "the model wrote it" is not an answer you would give.
Experience building LLM- or agent-backed capability into a production system — inference integration, tool and function calling, retrieval, evaluation, or guardrails — or clear evidence you can pick it up quickly.
Demonstrated experience building and scaling data or event pipelines in production.
Experience with CI/CD pipelines, infrastructure as code, and containerized workloads.
Experience with REST and/or GraphQL API design in distributed systems.
Experience carrying production on-call responsibility for systems you built.
Track record of leading a significant migration or platform initiative to completion.
Solid data modeling and systems design fundamentals, including designing for observability and cost.
Clear written and verbal communication, including the ability to explain technical trade-offs to non-technical partners.
Strong problem-solving, organizational, and leadership skills.
High initiative, self-motivation, and ability to mentor and inspire engineering teams.
Maintains confidentiality of proprietary information
Ability to maintain a professional atmosphere in a fast-paced environment with numerous interactions and interruptions
Ability to establish and maintain cooperative working relationships with Team Members and colleagues
Comfortable with office pets (cats/dogs).

Nice To Haves

Masters Degree
Familiarity with IoT systems, hardware integrations, or AWS IoT Core. You will work on a large connected-device platform here; we would rather teach it to a strong systems engineer than screen out on it.
Experience operating or migrating a connected-device fleet at scale.
Experience with Databricks, Spark, or a comparable lakehouse platform.
Experience with Cloudflare — Workers, CDN, DNS, or edge networking — or a comparable edge platform.
Experience with Amazon Bedrock or comparable managed inference.
Experience with MCP servers, agent harnesses, skill or prompt libraries, or evaluation frameworks — built, not just used.
Experience with Kafka or a comparable streaming platform.
Experience with Agile methodologies
Experience as a team leader
Familiarity with IoT systems / hardware integrations
Familiarity with JIRA, Confluence, or similar tools
Familiarity with Magento (1 or 2)

Responsibilities

Designs, builds, and operates shared backend services and platform capabilities that other engineering teams depend on
Owns service reliability end to end including participation in an on-call rotation, incident response, and the operational health of the systems you build
Establishes and improves the paved road: service templates, deployment pipelines, observability defaults, and infrastructure-as-code that make the right thing the easy thing for other teams
Designs for scalability, availability, and operational cost, and makes those trade-offs explicit rather than implicit
Extends the platform's AI capability including inference paths, agent infrastructure, and the evaluation and guardrail work that makes agent output trustworthy in production
Leads migrations of production systems serving live connected devices, sequencing the work so that customers are not disrupted and rollback remains possible
Models the failure modes that matter for connected hardware like a device that is offline, a firmware version that cannot be updated, a network that drops mid-command — and designs for them ahead of time rather than discovering them in QE
Retires legacy paths deliberately, including the dual-run, backfill, and verification work that makes a cutover safe
Builds and scales ingestion pipelines for high-volume device telemetry, routing it reliably into Whisker's data and analytics platform
Designs data contracts and schemas that survive change, and partners with Data Engineering, Data Science, and Analytics so the data arriving is the data they can actually use
Instruments the pipeline itself — freshness, completeness, cost — so problems are detected upstream of the dashboard that depends on them
Owns work that crosses systems and team boundaries, anticipating integration problems early and driving them to resolution
Surfaces risk early and proposes alternatives, rather than escalating a problem without a path forward
Holds a high quality bar in code review and closes out systemic defects rather than their latest instance.
Mentors engineers deliberately and raises the capability of the team around them
Represents the team's technical position credibly to Product, TPM, and partner engineering teams, translating trade-offs into terms non-engineers can decide against
Champions best practices in testing, code quality, security, and observability, and helps establish patterns other engineers build against
Works agentically by default by decomposing work so an agent can be steered and reviewed at each step, curating the context and skill files that make agent output good, and treating a generated diff as a draft to be understood rather than output to be forwarded
Improves how the team builds with AI: better skills, sharper review gates, and honest assessment of where agents help and where they do not
Will perform additional responsibilities when required