Position Overview
We are seeking an experienced Machine Learning Engineer to design and develop intelligent personalization and recommendation capabilities for consumer-facing applications and marketing initiatives.
This role will focus on using customer behavior, transactional activity, engagement signals, and other first-party data to create models that deliver more relevant experiences to individual consumers. The ideal candidate combines strong machine learning engineering fundamentals with hands-on experience developing recommendation, ranking, targeting, or personalization solutions at scale.
The position will work closely with engineering, data science, product, analytics, and marketing stakeholders to move models from experimentation through production deployment and ongoing optimization.
Key Responsibilities
Design, develop, and productionize machine learning models supporting recommendation, personalization, targeting, and next-best-action use cases.
Build solutions using techniques such as collaborative filtering, matrix factorization, learning-to-rank, neural networks, embeddings, and hybrid recommendation approaches.
Use behavioral, transactional, demographic, engagement, and other first-party data to improve the relevance of customer experiences.
Develop scalable ML solutions capable of processing large consumer datasets and supporting batch and real-time inference.
Partner with engineering teams to integrate machine learning models into applications, data pipelines, and customer engagement platforms.
Work with product, marketing, and analytics stakeholders to translate business objectives into measurable machine learning problems.
Establish appropriate evaluation frameworks for recommendation systems, including offline model metrics and business-oriented measures such as engagement, conversion, retention, and customer value.
Design and evaluate experiments, including A/B testing, to understand the real-world impact of personalization strategies.
Continuously monitor and improve model accuracy, performance, scalability, and relevance.
Explore emerging approaches including contextual bandits, reinforcement learning, graph-based recommendation techniques, and advanced deep-learning architectures.
Evaluate opportunities to incorporate large language models (LLMs) and agentic AI techniques into recommendation and personalization workflows.
Contribute to ML engineering standards and best practices around model development, deployment, monitoring, experimentation, and reproducibility.
Required Qualifications
5+ years of professional experience in machine learning, data science, or ML engineering, with meaningful experience developing recommendation or personalization systems.
Hands-on experience building recommendation, ranking, targeting, or next-best-action models used in production environments.
Strong Python development skills and experience with machine learning frameworks such as PyTorch, TensorFlow, or comparable technologies.
Strong knowledge of recommendation-system concepts and algorithms, including collaborative filtering, matrix factorization, embeddings, neural networks, ranking models, and hybrid approaches.
Experience working with large-scale behavioral, transactional, customer, or marketing datasets.
Demonstrated ability to evaluate machine learning solutions using both technical model metrics and measurable business outcomes.
Experience deploying and operating machine learning workloads in cloud-based environments.
Hands-on experience with AWS and Databricks.
Understanding of modern LLM architectures and agentic AI frameworks, including how these technologies can be adapted or incorporated into recommendation and personalization solutions.
Strong analytical and problem-solving skills with the ability to independently investigate complex data and modeling challenges.
Strong communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders.
Preferred Experience
Previous experience within e-commerce, retail, digital media, consumer technology, marketing technology, or another high-volume consumer environment.
Experience with customer segmentation, audience targeting, marketing analytics, or customer data platforms.
Experience designing or analyzing controlled experiments and A/B tests.
Familiarity with real-time model serving and low-latency recommendation architectures.
Experience with contextual bandits, reinforcement learning, graph neural networks, or other advanced recommendation techniques.
Experience applying LLMs, generative AI, or AI agents to personalization or customer-engagement use cases.
Ideal Candidate
The strongest candidate will be more than a general-purpose machine learning engineer. They will have direct experience building recommendation or personalization systems and will understand how model design decisions ultimately affect customer behavior and business outcomes.
They should be comfortable moving between experimentation and production engineering, working with large and imperfect consumer datasets, and explaining why a particular modeling approach is appropriate for a given recommendation problem.

Machine Learning Engineer

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