About Angler AI

Angler AI is a seed-stage startup that powers growth for consumer companies in the wake of privacy changes. Our SaaS app connects to Ecommerce merchants’ and DTC brands’ data, applies our proprietary AI models to predict conversions and LTV, and distributes these predictions to ad platforms like Meta and Google, boosting campaign ROAS by 28% on average. We are tier-1 VC-backed and are already seeing amazing results for 40+ customers.

About the Role

Angler is looking for a Machine Learning (ML) engineer to lead the development, deployment, and operations of automated data pipelines, ML models, and cloud infrastructure that underpin our SaaS application. Ideally you have experience building both batch and streaming data pipelines using AWS and/or GCP services, so that our ML models have reliable, high-quality, up-to-date source data. Our models solve classification, regression, and ranking problems and are applied to a variety of customer acquisition, personalization, and retention use cases. You also have built large-scale models in Spark and are able to make architecture and technical requirement decisions to establish repeatable patterns. And of course, you’re comfortable with the fast-paced environment of an early-stage startup, while collaborating with both full-time and outstaffed engineers across time zones.

Responsibilities

Collaborate with our small team including a product manager, cloud architect, full-stack engineer, and data scientist to define requirements and data/model pipeline specifications.
Develop, deploy, and maintain both cluster-based and serverless data pipelines using AWS/GCP components that enable data streams, serverless functions, workflows, Spark jobs, analytical data warehouses, and relational databases.
Design, develop, and iterate ML prototype models that meet necessary quality metrics, following a methodical approach to data selection, dataset preparation, feature engineering, experimentation, statistical analysis, and tuning.
Extend existing ML libraries and frameworks where appropriate.
Play the role of data modeling and querying expert and make recommendations regarding standards for storage format, query engine, schema design, and code quality.

Skills & Qualifications

Bachelor’s degree in computer science, engineering, statistics, math, economics, information technology or related discipline. Advanced degree (MS, Ph.D.) preferred.
5+ years prior experience as a Data Engineer or ML Engineer with at least one end-to-end concept-to-production deployment.
Deep understanding of tree-based models such as XGBoost; experience with XGBoost on Spark is a plus.
Proficiency in Spark with solid understanding of parallel data processing.
Experience with tuning Spark jobs for optimizing for performance while controlling infrastructure costs.
Proficiency in Python (including a solid understanding of Python data structures) and SQL.
Familiarity with AWS and/or GCP services, along with the ability to quickly learn complex domains and new technologies.
Understanding of data modeling and software architecture.
Demonstrated ability to analyze large data sets to identify gaps and inconsistencies, provide data insights, and advance effective product solutions.
Thrives in a fast-paced startup environment.
Must be currently authorized to work in the US on a full-time basis.

Nice To Have

Experience with Scala.
Experience using ML frameworks (like Keras or PyTorch) and libraries (like scikit-learn).
Experience using Jira, GitHub, CDK.
Experience with Ecommerce platforms such as Shopify, Magento or Salesforce Commerce.
Experience with marketing data such as Facebook custom audiences and conversion API.
Experience with BI tools such as Amazon Quicksight.
Experience with data quality processes, data quality checks, validations and measurement.

Benefits

Medical insurance
Dental benefits
Vision benefits
Unlimited PTO & sick days
401k with 4% employer match

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