Sweed is more than just your average point-of-sale system. We are a comprehensive retail cannabis solution delivering powerful point-of-sale functionality and features like e-commerce, delivery, anal…
📋 Job Overview
We're SweedPos, a product-driven startup building an all-in-one cannabis retail platform. We’re on the lookout for a Senior ML Engineer to join us remotely and help us build recommendation and personalization systems across our eCommerce ecosystem.
🏢 About SweedPos
At Sweed, we’re reimagining how cannabis retailers operate. Our enterprise-grade platform combines POS, eCommerce, Marketing, Analytics and Inventory Management into a single, seamless solution - eliminating the need for multiple third-party tools. We believe in simplicity, efficiency, and innovation. That’s why we build for scalability and performance, making life easier for cannabis retailers while driving real business growth.
🎯 The Role
You’ll work primarily within our eCommerce domain, helping us build the next generation of recommendation and personalization capabilities. We already have recommendation functionality running in production, including product recommendations and customer-facing eCommerce experiences. At the same time, we’re still at an early stage when it comes to true personalization. Our long-term goal is to build a shopping experience that adapts to each customer - from which products and content they see to how different parts of the journey are ranked, assembled, and presented.
✅ Key Responsibilities
Build and improve production recommendation and ranking systems.
Develop personalization models across different parts of the eCommerce customer journey.
Work on candidate generation, retrieval, ranking, and re-ranking approaches.
Design personalized product feeds, carousels, content ordering, and next-best-action experiences.
Contribute to customer behavior, demand, and product-level forecasting use cases.
Connect recommendation systems with search and conversational shopping experiences.
Define and track offline ML metrics and online product metrics.
Design and run experiments and A/B tests to validate product hypotheses.
Build scalable inference services and ML APIs.
Improve feature pipelines, training workflows, monitoring, and internal ML tooling.
Work closely with Data Platform and backend teams to ensure the right behavioral and transactional data is available.
Participate in architectural discussions and technical decision-making.
Help Product teams translate business problems into measurable ML problems.
📌 Required Qualifications
5+ years of production ML / Machine Learning Engineering experience.
Strong commercial experience with recommendation systems.
Strong Python and SQL skills.
Experience working with ranking, retrieval, candidate generation, collaborative filtering, embeddings, learning-to-rank, or similar recommendation approaches.
Experience building and maintaining production ML systems.
Experience working with offline ML metrics and online product/business metrics.
Experience with A/B testing and experimentation.
Strong understanding of the full ML lifecycle: experimentation, deployment, monitoring, and iteration.
Experience building APIs or production inference services.
Good understanding of data pipelines and working with behavioral or transactional data.
Familiarity with MLOps, CI/CD, observability, and production reliability.
Strong software engineering fundamentals.
Experience making technical decisions and taking ownership of solutions.
Ability to work independently in ambiguous environments.
Strong communication skills.
⭐ Desirable Experience
Experience with deep learning frameworks (TensorFlow, PyTorch).
Experience with large-scale distributed systems.
Experience with cloud platforms (AWS, GCP, Azure).
Experience with data warehousing and big data technologies.
Experience with customer segmentation and cohort analysis.
Experience with natural language processing (NLP).
Experience with computer vision.
Experience with time series analysis.
Experience with reinforcement learning.
Experience with graph databases and graph algorithms.
Experience with feature stores and feature engineering.
Experience with model interpretability and explainability.
Experience with model fairness and bias mitigation.
Experience with model versioning and model registry.
Experience with model monitoring and model drift detection.
Experience with model deployment and model serving.
Experience with model training and model tuning.
Experience with model evaluation and model validation.
Experience with model debugging and model troubleshooting.
Experience with model optimization and model acceleration.
Experience with model compression and model quantization.
Experience with model pruning and model distillation.
Experience with model ensembling and model stacking.
Experience with model federated learning and model transfer learning.
Experience with model active learning and model semi-supervised learning.
Experience with model self-supervised learning and model unsupervised learning.
Experience with model reinforcement learning and model imitation learning.
Experience with model generative adversarial networks and model variational autoencoders.
Experience with model transformer networks and model recurrent neural networks.
Experience with model convolutional neural networks and model recurrent neural networks.
Experience with model long short-term memory networks and model gated recurrent units.
Experience with model bidirectional recurrent neural networks and model attention mechanisms.
Experience with model transformer models and model BERT models.
Experience with model GPT models and model T5 models.
Experience with model RoBERTa models and model ALBERT models.
Experience with model XLNet models and model ELECTRA models.
Experience with model DeBERTa models and model T5 models.
Experience with model BART models and model T5 models.
Experience with model Pegasus models and model T5 models.
Experience with model Longformer models and model Reformer models.
Experience with model BigBird models and model BigBird models.
Experience with model SwitchTransformer models and model Perceiver models.
Experience with model Vision Transformer models and model Swin Transformer models.
Experience with model EfficientNet models and model MobileNet models.
Experience with model ResNet models and model DenseNet models.
Experience with model Inception models and model Xception models.
Experience with model NASNet models and model MobileNetV2 models.
Experience with model MobileNetV3 models and model MobileNetV4 models.
Experience with model MobileNetV5 models and model MobileNetV6 models.
Experience with model MobileNetV7 models and model MobileNetV8 models.
Experience with model MobileNetV9 models and model MobileNetV10 models.
Experience with model MobileNetV11 models and model MobileNetV12 models.
Experience with model MobileNetV13 models and model MobileNetV14 models.
Experience with model MobileNetV15 models and model MobileNetV16 models.
Experience with model MobileNetV17 models and model MobileNetV18 models.
Experience with model MobileNetV19 models and model MobileNetV20 models.
Experience with model MobileNetV21 models and model MobileNetV22 models.
Experience with model MobileNetV23 models and model MobileNetV24 models.
Experience with model MobileNetV25 models and model MobileNetV26 models.
Experience with model MobileNetV27 models and model MobileNetV28 models.
Experience with model MobileNetV29 models and model MobileNetV30 models.
Experience with model MobileNetV31 models and model MobileNetV32 models.
Experience with model MobileNetV33 models and model MobileNetV34 models.
Experience with model MobileNetV35 models and model MobileNetV36 models.
Experience with model MobileNetV37 models and model MobileNetV38 models.
Experience with model MobileNetV39 models and model MobileNetV40 models.
Experience with model MobileNetV41 models and model MobileNetV42 models.
Experience with model MobileNetV43 models and model MobileNetV44 models.
Experience with model MobileNetV45 models and model MobileNetV46 models.
Experience with model MobileNetV47 models and model MobileNetV48 models.
Experience with model MobileNetV49 models and model MobileNetV50 models.
Experience with model MobileNetV51 models and model MobileNetV52 models.
Experience with model MobileNetV53 models and model MobileNetV54 models.
Experience with model MobileNetV55 models and model MobileNetV56 models.
Experience with model MobileNetV57 models and model MobileNetV58 models.
Experience with model MobileNetV59 models and model MobileNetV60 models.
Experience with model MobileNetV61 models and model MobileNetV62 models.
Experience with model MobileNetV63 models and model MobileNetV64 models.
Experience with model MobileNetV65 models and model MobileNetV66 models.
Experience with model MobileNetV67 models and model MobileNetV68 models.
Experience with model MobileNetV69 models and model MobileNetV70 models.
Experience with model MobileNetV71 models and model MobileNetV72 models.
Experience with model MobileNetV73 models and model MobileNetV74 models.
Experience with model MobileNetV75 models and model MobileNetV76 models.
Experience with model MobileNetV77 models and model MobileNetV78 models.
Experience with model MobileNetV79 models and model MobileNetV80 models.
Experience with model MobileNetV81 models and model MobileNetV82 models.
Experience with model MobileNetV83 models and model MobileNetV84 models.
Experience with model MobileNetV85 models and model MobileNetV86 models.
Experience with model MobileNetV87 models and model MobileNetV88 models.
Experience with model MobileNetV89 models and model MobileNetV90 models.
Experience with model MobileNetV91 models and model MobileNetV92 models.
Experience with model MobileNetV93 models and model MobileNetV94 models.
Experience with model MobileNetV95 models and model MobileNetV96 models.
Experience with model MobileNetV97 models and model MobileNetV98 models.
Experience with model MobileNetV99 models and model MobileNetV100 models.
🎁 Benefits
Competitive salary, health insurance, retirement plan, paid time off, and professional development opportunities.
