Everseen: A leader in vision AI solutions for the world’s leading retailers.
The Role
At Everseen, we are scaling the global ML Platform powering our computer vision lifecycle. As a Senior SDE III, you will design and deliver the strategic, high-impact MLOps services that accelerate engineering iteration and bring transparency to our massive GPU workloads.
You’ll navigate architectural ambiguity, partner closely with AI Research, and mentor junior team members to raise our overall engineering bar.
You will get the opportunity to work on:
- Scale Inference & MLOps: Build high-performance, shared inference services with unified model catalogs, priority queuing, and full call auditability.
- Empowering AI Scientists: Architect the robust training infrastructure and registries that make every global training run 100% reproducible.
- Trace Data Lineage: Connect our platform ecosystem by extending lineage tracking and designing stable, reliable API contracts.
- Optimize GPU Utilization: Create the multi-tenant cost-tracking layers, quotas, and fairness primitives across our massive GPU clusters.
- Platform Craft: Design APIs, SDKs and React dashboards with the same rigor you would apply to an external commercial SaaS product.
Everseen: A leader in vision AI solutions for the world’s leading retailers.
AboutEverseen
Shrink and inefficiency cost global retailers more than a hundred billion dollars each year. But Everseen is changing that equation. We are a leader in vision AI, transforming business operations for global retailers by reducing shrink, improving operations, and delivering a better customer experience.
Supporting 11 of the top 20 global retailers, Everseen’s AI innovations help retailers save millions of dollars every month. Its flagship product, Evercheck, is deployed in 10,000 retail stores and 150,000 checkouts globally. Everseen operates at unmatched scale, processing over 15 million transactions, and more than six petabytes of video data, daily.
Trusted by major food, drug, mass, and specialty retailers around the world - including Kroger, Meijer, and Woolworths - we also partner with leading hardware, AI, and cloud computing providers such as Google, NVIDIA, NCR, and Dell.
Founded in 2007 and headquartered in Cork, Ireland, Everseen has over 700 employees globally, with a European headquarters in Cork, Ireland, a U.S. headquarters in Miami, and hubs in Romania, Serbia, India, Australia, and Spain.
We are a dedicated team of inventors, research scientists, engineers, AI experts and retail industry veterans. Our culture is built on accountability, collaboration, and high standards. We take ownership of outcomes, prioritise our customers, operate as one team, move quickly with purpose, and continually raise the bar in everything we deliver.
Our Culture
Our culture is built on ownership and accountability, we own outcomes, not tasks. We stay relentlessly customer-first, letting their needs shape our priorities and decisions.
We work as one team: we challenge ideas, align quickly, and commit together. We move with pace, without compromising on simplicity or quality. And every day, we raise the bar, strengthening our talent, pushing innovation forward, and maximizing our impact.
Our Values
We OWN THE OUTCOME. No silos, no excuses.
We ARE CUSTOMER-FIRST. They guide our decisions.
We ARE ONE TEAM. Debate, decide, commit.
We MOVE WITH PACE. With simplicity and quality.
We RAISE THE BAR. On talent, innovation and impact.
Our Commitment
Everseen is committed to creating an environment where everyone can succeed. Our employees should feel a sense of belonging, have an opportunity to grow their careers, and feel free to be their most authentic selves. Everseen takes great pride in the diversity of its global workforce, and insists upon a safe, inclusive workplace where our differences are our collective strength. We treat each other with dignity, and respect, and require all employees, officers, and directors to seek to understand the importance and value to Everseen of diversity, and inclusion.
Everseen is committed to creating a safe environment for all employees and has a zero tolerance policy for bias and discrimination of any kind. Our work environment is one without offensive, hostile, or intimidating conduct, whether verbal, written or physical, in nature. Everseen will not tolerate prejudice or discrimination of any kind including without limitation, where based on aspects such as, race, colour, sex, gender, religion, age, family status, disability of any kind, sexual orientation.
Our Technology Stack
As an Engineer with the ML Platform team at Everseen, you’ll have the opportunity to work with and develop your skills across a modern, high-performance tech stack:
Core: Python on the backend and TypeScript on the front end, running on Kubernetes and Docker on Azure, with MongoDB as the system of record.
MLOps: model serving, experiment tracking, and workflow orchestration — we're standardizing on tools like Ray Serve, MLflow, and Airflow.
Scale & hardware: large-scale video-data pipelines, GPU orchestration, and Infrastructure as Code with Terraform.
Distributed training: federated learning (e.g., Flower) for training across data that can't be centralized.
Lineage: end-to-end tracking from data → training → models → evaluation — the backbone that makes the whole platform reproducible and auditable.
Platform UI: TypeScript and React for the dashboards, evaluation browsers, model-registry views, and annotation tooling teams use to run the platform.
What you’ll do
Design & Development
Owns a core sub-system, a significant slice of a technology pillar, or complex business-critical features.
Leads the design of complex features, services, or new sub-systems (e.g., component contracts, interface/API designs, SDKs, or high-throughput data pipelines).
Directly drives progress on highly complex tasks that involve multiple cross-functional dependencies, high business impact, or ambiguous requirements. Support vulnerability scanning, management, penetration testing, and incident response.
Influence & Decision-Making
Influences team-level architecture, implementation approaches, and continuous technical improvements.
Contributes significantly to strategic technical decisions within your specific product, platform, or service area.
Aligns technical consensus across team members and represents your team's sub-system in cross-pillar/cross-team discussions.
Coding & Software Craftsmanship
Contributes hands-on to the most technically challenging parts of the team's workload.
Designs and implements highly reusable, efficient, and elegant code built for complex requirements and long-term system strategy.
Quality, Testing & CI/CD
Improves team-level software quality by establishing stronger test coverage, advanced validation patterns, and proactive defect prevention.
Drives improvements in team CI/CD processes and release quality through automated build, test, and deployment practices.
Monitoring, Troubleshooting & Data Analysis
Leads the diagnosis and troubleshooting of complex production issues within team systems, improving system resilience and observability.
Utilizes production diagnostics, logs, stack traces, and system metrics to identify trends, isolate root causes, and propose engineering improvement opportunities.
Research, evaluate, and propose third-party software solutions to optimize system performance and expand capabilities.
Documentation
Responsible for creating, reviewing, and maintaining high-quality technical documentation and durable design knowledge to ensure systems are easily understood and accessible.
Teaching & Sharing Culture
Mentors' engineers (SDE I/II) support their technical growth and reviews peers' designs.
Proactively shares skills, knowledge, and technical expertise with members of the engineering team to raise the overall quality bar.
Actively fosters a culture of collaboration, open communication, and continuous learning within the team.
Collaborating With
AI Engineering: Our primary consumers — you build the training orchestration, model serving, and registry they use to ship product models, and the dataset and lineage plumbing underneath.
Research: You give ML researchers fast, reproducible experimentation — experiment tracking, GPU access, and the evaluation harness that turns ideas into measured results.
Operations: You partner with the teams running models at the edge — collecting data and helping them measure model performance.
Annotation: You support the annotators who label our data with the annotation tooling they work in every day.
Profile and Skills
Experience & Education Requirements
Technical Skills & Competencies
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Programming Proficiency: In-depth knowledge of at least one relevant language (e.g., Python, JavaScript/TypeScript, C++), with strong programming skills to build and operate robust services.
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Cloud & Infrastructure: Strong understanding of major cloud platforms, including container orchestration, storage services, and cloud security and scalability principles. Excellent understanding of cloud computing service models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) architecture.
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Best Practices: Solid understanding of engineering best practices, CI/CD pipelines, observability setups, and operational readiness.
Soft Skills & Leadership Attributes
