Jobs at Constructor

Lead Constructor's end-to-end security program: manage compliance (SOC 2, ISO 27001), incident response, risk and access governance, vendor security, DLP, AI governance, security exercises, and customer-facing security communications while partnering with platform engineering on cloud and container security.

Lead product analytics by defining metrics, building metric frameworks, analyzing funnels and user behavior, designing and analyzing A/B tests, setting up instrumentation and dashboards, and partnering with product, engineering, and leadership to turn data into product strategy and measurable business impact.

Design and analyze A/B tests, apply statistics and machine learning to improve search, ranking, personalization and recommendations, analyze large-scale behavioral data, build models and tools, and communicate findings to technical and business stakeholders to drive key business metrics.

Design and build large-scale data pipelines and models, create dashboards and tools, define and validate business metrics, ensure data quality and performance across distributed systems, and partner with analysts, engineers, and product managers to deliver actionable analytics.

Lead product strategy for Query and Machine Learning & Recall teams to improve candidate retrieval and query understanding. Define roadmap, productionize SOTA models (vector search, embeddings, LLM-assisted parsing), build evaluation frameworks linking retrieval accuracy to revenue, optimize inference latency and costs, and drive measurable lifts in conversion and revenue.

Provide enterprise customers with technical support across APIs, SDKs, data integrations, event instrumentation, tagging, logs, and implementation behavior. Troubleshoot issues, manage customer communications, coordinate escalations, and explain resolutions clearly. Improve support processes, automation, documentation, knowledge bases, and escalation paths while sharing recurring issues with Product and Engineering. Support EMEA customers during regional business hours and collaborate with technical and business stakeholders.

Manage a portfolio of enterprise SaaS customers from onboarding through renewal and expansion. Build VP- and C-suite relationships, understand ecommerce business goals, lead complex renewals and upsells, identify customer risks, conduct Quarterly Business Reviews, and educate clients on new use cases and integrations. Collaborate cross-functionally with Sales, Customer Success, Solutions Engineering, Data Science, Product, and Engineering teams while documenting customer health, needs, and opportunities.

Design the demo and onboarding experiences for Constructor's e-commerce product discovery platform. Own visual strategy, map interactions for AI/LLM features, produce high-fidelity mockups and assets, and ensure designs scale within the company design system to help customers go live faster.

Lead product marketing for Constructor's ecommerce search and discovery platform: define positioning, messaging, launches, competitive intelligence, and build a PMM team. Partner with Product, Sales, Growth, and Content to translate complex features into customer value, enable GTM, and drive pipeline and adoption.

Lead a cross-functional personalization team building production ML systems for search and browse. Own model design, user representations, embeddings, segmentation, offline evaluation, experimentation, and metrics-driven iteration. Hire, mentor, partner with product/engineering, and contribute technically to architecture and implementation to drive measurable retailer KPI improvements.

Analyze large-scale user and product data, design and evaluate A/B tests, define and validate product and business metrics, build dashboards and reports, and partner with engineers, data scientists, product, and customers to translate insights into product improvements.

Analyze and instrument offsite discovery and SEO/GEO performance for ecommerce sites. Build integrations, ETL pipelines, and models to track organic and AI-driven traffic. Create tooling and frameworks for technical site audits, landing page modelling, SEO experiments, and enablement for product and customer teams.

Lead and scale a Customer Success team across EMEA to drive customer adoption, retention, and expansion. Ensure successful go-lives, build executive relationships, implement scalable processes, act as escalation point for complex issues, partner with Product and Engineering to prioritise customer-impacting work, and continuously improve onboarding and optimisation to maximise customer value.

Lead and scale the Offsite Discovery engineering team to build a high-performance, cost-efficient real-time personalization platform. Set engineering strategy, partner with product, hire and mentor engineers, define metrics and alerting, improve operational excellence, and collaborate across teams to drive measurable improvements in performance, cost, and product capabilities.

Design, build, and maintain scalable backend services that deliver ML-enriched product attributes and badges. Develop high-throughput, low-latency APIs and manage a dedicated database, CI/CD pipelines, observability, and tests. Collaborate with ML engineers and cross-functional teams to ensure reliability, performance, and availability for downstream systems and customer dashboards.

Design, deliver, and maintain high‑load real‑time recommendation web services. Implement IaC, CI/CD, observability, and stability testing. Collaborate with cross‑functional teams, integrate NoSQL/SQL and external services, and migrate features into distributed services.

Design, build, deploy, and maintain high-load, real-time ML-based ranking services and a Ranking Platform. Improve performance, feature delivery (feature store), CI/CD, and collaborate with DS, MLEs, and stakeholders.

Build, deliver, and maintain a customer dashboard using TypeScript and React that interacts with Python/FastAPI backend services. Collaborate with cross-functional teams, design APIs, ensure high-scale performance, analyze dashboard metrics using SQL, and contribute product ideas and stakeholder communications.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.