Everything we do is for our customers!
Featured on Deloitte's Technology Fast 500 list and G2's leaderboard, Maropost offers a unified commerce experience that our customers need, transforming ecommerce, retail, marketing automation, merchandising, helpdesk and AI operations with one platform designed to scale for fast-growing businesses. With a relentless focus on our customers’ success, we are motivated by customer obsession, extreme urgency, excellence and resourcefulness to power 5,000+ global brands while we head to 100,000+.
Driven by the same customer-centric mentality as above, we empower businesses to achieve their goals and grow alongside us. If you're a driver and not passenger and are ready to make a significant impact and be part of our transformative journey, Maropost is the place for you.
The Opportunity:
You will own the machine learning that powers product discovery for our merchants: personalized search ranking, recommendation widgets, email recommendations, and an LLM shopping assistant. This is an end-to-end role. You will train the models, build the services that serve them, and keep both running in production. You will not hand a model over a wall to someone else.
The team is small and the systems are real: Recommendations are re-ranked in the live request path for storefronts, under a 200 ms budget, measured by merchant conversion.
What Youʼll Be Responsible For:
• Design, train and ship recommendation and ranking models - session-based embeddings, collaborative filtering, content and visual embeddings.
• Build and operate the Python services that serve them at low latency.
• Run the batch pipelines that rebuild model artifacts daily across hundreds of merchants.
• Design and read AB tests; decide what ships on the evidence.
• Own your deployments: containers, Kubernetes manifests, autoscaling, dashboards, alerts.
• Extend our LLM work - a shopping assistant and LLM-assisted catalogue enrichment - with proper evaluation behind it.
What Youʼll Bring to Maropost:
• 5+ years building machine learning systems, with at least 3 years of models serving live production traffic.
• Strong Python,(FastAPI or equivalent).
• Vector databases (Qdrant).
• Other ANN libraries - FAISS, Annoy, ScaNN. HNSWlib is what we run, but the trade-offs transfer.
• Hands-on recommender systems or search ranking experience: implicit feedback, embeddings, approximate nearest neighbour search.
• Solid SQL against analytical stores; ClickHouse experience is a plus.
• Comfortable with Docker and Kubernetes, and willing to own the deployment of your own work.
• Experience running AB tests and reporting results you did not like.
• Streaming systems (Pulsar, Kafka, Flink)
• You exemplify Maropost’s Values:
Customer Obsessed
Extreme Urgency
Excellence
Resourceful
Bonus Points:
• PyTorch, sentence-transformers and CLIP, or computer vision applied to product imagery.
• Gradient boosting for ranking (XGBoost, LightGBM or CatBoost). We run a legacy XGBoost autocomplete ranker and expect to revisit learned ranking.
• Scikit-learn and scipy for lightweight classifiers and experiment statistics.
• LLM application work with evaluation harnesses, tool calling, and cost and latency tuning.
• E-commerce, search relevance, or marketplace background.
• Python async experience
• Experience around search
Our stack:
Python 3.11–3.13, FastAPI, gensim, PyTorch, sentence-transformers and CLIP, HuggingFace transformers, XGBoost, scikit-learn, scipy, pandas, numpy, HNSWlib, Qdrant, ClickHouse, Redis, MySQL, Postgres, Pulsar, Docker, Kubernetes on GCP, ArgoCD, CircleCI, Grafana, Sentry, and Gemini on Vertex via pydantic-ai.
Message from the Founders: Maropost is looking for builders - people who want to drive our business forward at all costs in order to achieve the goals we have both short and long term for the results and outcomes that that will bring to us all.
If that isn't for you that’s ok, for those of you that it is please get in touch with us!
Originally posted on Himalayas
Everything we do is for our customers!
Featured on Deloitte's Technology Fast 500 list and G2's leaderboard, Maropost offers a unified commerce experience that our customers need, transforming ecommerce, retail, marketing automation, merchandising, helpdesk and AI operations with one platform designed to scale for fast-growing businesses. With a relentless focus on our customers’ success, we are motivated by customer obsession, extreme urgency, excellence and resourcefulness to power 5,000+ global brands while we head to 100,000+.
Driven by the same customer-centric mentality as above, we empower businesses to achieve their goals and grow alongside us. If you're a driver and not passenger and are ready to make a significant impact and be part of our transformative journey, Maropost is the place for you.
The Opportunity:
You will own the machine learning that powers product discovery for our merchants: personalized search ranking, recommendation widgets, email recommendations, and an LLM shopping assistant. This is an end-to-end role. You will train the models, build the services that serve them, and keep both running in production. You will not hand a model over a wall to someone else.
The team is small and the systems are real: Recommendations are re-ranked in the live request path for storefronts, under a 200 ms budget, measured by merchant conversion.
What Youʼll Be Responsible For:
• Design, train and ship recommendation and ranking models - session-based embeddings, collaborative filtering, content and visual embeddings.
• Build and operate the Python services that serve them at low latency.
• Run the batch pipelines that rebuild model artifacts daily across hundreds of merchants.
• Design and read AB tests; decide what ships on the evidence.
• Own your deployments: containers, Kubernetes manifests, autoscaling, dashboards, alerts.
• Extend our LLM work - a shopping assistant and LLM-assisted catalogue enrichment - with proper evaluation behind it.
What Youʼll Bring to Maropost:
• 5+ years building machine learning systems, with at least 3 years of models serving live production traffic.
• Strong Python,(FastAPI or equivalent).
• Vector databases (Qdrant).
• Other ANN libraries - FAISS, Annoy, ScaNN. HNSWlib is what we run, but the trade-offs transfer.
• Hands-on recommender systems or search ranking experience: implicit feedback, embeddings, approximate nearest neighbour search.
• Solid SQL against analytical stores; ClickHouse experience is a plus.
• Comfortable with Docker and Kubernetes, and willing to own the deployment of your own work.
• Experience running AB tests and reporting results you did not like.
• Streaming systems (Pulsar, Kafka, Flink)
• You exemplify Maropost’s Values:
Customer Obsessed
Extreme Urgency
Excellence
Resourceful
Bonus Points:
• PyTorch, sentence-transformers and CLIP, or computer vision applied to product imagery.
• Gradient boosting for ranking (XGBoost, LightGBM or CatBoost). We run a legacy XGBoost autocomplete ranker and expect to revisit learned ranking.
• Scikit-learn and scipy for lightweight classifiers and experiment statistics.
• LLM application work with evaluation harnesses, tool calling, and cost and latency tuning.
• E-commerce, search relevance, or marketplace background.
• Python async experience
• Experience around search
Our stack:
Python 3.11–3.13, FastAPI, gensim, PyTorch, sentence-transformers and CLIP, HuggingFace transformers, XGBoost, scikit-learn, scipy, pandas, numpy, HNSWlib, Qdrant, ClickHouse, Redis, MySQL, Postgres, Pulsar, Docker, Kubernetes on GCP, ArgoCD, CircleCI, Grafana, Sentry, and Gemini on Vertex via pydantic-ai.
Message from the Founders: Maropost is looking for builders - people who want to drive our business forward at all costs in order to achieve the goals we have both short and long term for the results and outcomes that that will bring to us all.
If that isn't for you that’s ok, for those of you that it is please get in touch with us!
Originally posted on Himalayas