Staff+ Forward Deployed Research Engineer — Search & Recommendations

ABOUT US

Building the ranking intelligence layer for the internet.

Sequen provides an integrated platform that pairs cutting-edge frontier ranking models with the infrastructure to run them in production at enterprise scale. The world's largest retailers, marketplaces, and travel platforms use Sequen to rank, recommend, and personalize, with an autonomous research engine that compounds model performance into revenue and margin lift measured in hundreds of millions of dollars per customer.

We are a small, highly technical, early-stage team focused on turning recent advances in AI into production-grade systems that operate under unforgiving real-world constraints. The problems we work on are deeply open-ended, where minor optimizations in algorithmic multi-stage retrieval and model routing translate directly into millions of dollars in client revenue.

ABOUT THE ROLE

We’re looking for a Staff+ Forward Deployed Research Engineer with deep expertise in search and recommendation modeling to help us design, develop, and scale innovative ranking, embedding, and retrieval systems that power our personalized discovery platform.

In this role, you’ll have the unique opportunity to work at the intersection of cutting-edge AI research and production-grade software systems. You will work directly within our customers' digital environments—helping them translate user behavior data into direct revenue and conversion lift. This is an ideal role for someone eager to take end-to-end ownership of highly complex ML lifecycles and operate within an agile, elite startup environment.

Key Responsibilities

Deploy production models: Work directly within customer environments to design, build, and deploy production ranking and recommendation models that directly optimize for business conversion, engagement, and lifetime value.

Deliver technical artifacts: Engineer end-to-end ranking pipelines, embedding generation schemas, nearest-neighbor retrieval systems, and low-latency model serving infrastructure.

Provide white-glove partnership: Deliver high-touch deployment support inside large consumer enterprise environments, ensuring seamless integration with existing data ecosystems and business logic.

Architect data pipelines: Design and maintain production-grade, highly optimized data pipelines to process massive datasets, ensuring models are continuously fed with fresh, reliable training and inference features.

Codify repeatable patterns: Identify and modularize repeatable deployment patterns across various industry verticals, contributing key insights back to Sequen's core product and engineering teams.

Own production lifecycles: Manage the entire ML lifecycle end-to-end, including model monitoring, A/B testing frameworks, performance regression detection, and continuous retraining strategies.

Drive research frontiers: Maintain deep, active expertise in the latest developments in deep learning, including multi-stage retrieval, learning-to-rank, and large-scale personalization techniques.

Uncover organic expansion: Build strong relationships with client engineering teams and proactively identify new opportunities to deploy ranking solutions that unlock additional customer value.

Champion Sequen's mission: Act as a technical representative of Sequen AI's vision to shape end-user behavior for the world's largest consumer platforms.

ABOUT YOU

Proven track record: Bring 7+ years of experience as an ML Engineer, Applied Scientist, or Forward Deployed Engineer with a heavy focus on deep learning for search, recommendation, or retrieval systems.

Model building mastery: Possess deep proficiency in PyTorch, deep learning theory, and machine learning algorithms (specifically multi-stage scoring and embedding models).

Data pipeline proficiency: Demonstrate hands-on experience designing and optimizing large-scale, distributed data pipelines using Spark, Airflow, and dbt across GCP and AWS environments.

Cloud & infra maturity: Operate comfortably with Kubernetes, Airflow, Terraform, and cloud platforms like AWS and GCP to manage scalable model deployment and experimentation environments.