Senior Machine Learning Engineer

Rokt; Austin (TX); Híbrido; USD 210 000 - 260 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

World-class employee benefits

Resumo da oferta

Rokt is seeking Senior Machine Learning Engineers to design, build, and productionise proprietary ML models for smart bidding, lookalike modelling, forecasting, and ranking tasks.

Join a team that collaborates with product managers and engineers to solve business challenges, with a 4-day in-office + 1-day remote schedule and a total comp range of $335k–$400k, including equity and comprehensive benefits.

Qualificações

• PhD or Master’s in CS/Statistics/Math with ML specialization.
• 3+ years of industry experience building production-grade ML systems.
• Experience with Kubeflow (or similar), TensorFlow, and a feature store in production.

Responsabilidades

• Collaborate with product managers and engineers to frame ML problems and architect solutions.
• Build and productionise ML models, data pipelines, and feature engineering in the feature store.

Conhecimentos

Bayesian methods

Recommender systems

Meta-learning

CTR modelling

Multi-task modelling

Formação académica

PhD or Master’s in CS/Statistics/Math

Ferramentas

Kubeflow

TensorFlow

Feature store

Descrição da oferta de emprego

Rokt is an ecommerce technology company with the mission of making every transaction more relevant. The Rokt Ecommerce Network leverages proprietary machine learning recommendation systems, powering billions of transactions for hundreds of millions of customers, and is trusted to do this by companies like Live Nation, Fanatics, Macy's, AMC Theatres, PayPal, Uber, Hulu, Staples, Albertsons and HelloFresh.

We are hiring Senior Machine Learning Engineers

We are hiring engineers with significant expertise in both machine learning and software engineering. You will be working with our engineering and product teams to design, build and productionise proprietary machine learning models to solve different business challenges including smart bidding, lookalike modelling, forecasting, etc.

Target total compensation ranges from $335k - $400k, comprised of a fixed annual salary of $210k - $260k, plus employee equity plan grant. In addition, you will receive world-class employee benefits.

• Collaborate closely with product managers and other engineers to understand business priorities, frame machine learning problems, and architect machine learning solutions for smart bidding, lookalike modelling, forecasting, and related ranking and prediction tasks.
• Build and productionise machine learning models, including model-specific data pipelines, feature engineering within the team's feature store, and integration with the team's orchestration and serving infrastructure.
• Evaluate model performance through offline metrics, and monitor deployed models for drift, leading retraining or rollback decisions as needed.
• Contribute to and maintain the high quality of the code base with tests that provide a high level of functional coverage as well as non-functional aspects such as load testing, unit testing, and integration testing.
• Keep track of emerging tech and trends, research the state-of-the-art deep learning models, prototype new modelling ideas, and conduct offline and online experiments
• Willingness to work 4 day in-office, 1 day remote weekly schedule.

• PhD or Master's in Computer Science, Statistics, Mathematics, or related field with specialization in ML, AI, or Information Retrieval (or equivalent experience)
• Extensive knowledge in and experience with some of the following areas: Bayesian methods, recommender systems, multi-task modelling, meta-learning, click-through rate modelling or conversion rate modelling
• 3+ years of industry experience building production-grade machine learning systems, spanning model training, tuning, deployment, serving, and monitoring
• Experience with Kubeflow (or similar), TensorFlow, and a feature store in a production environment is a massive plus
• Bonus points if you are familiar with any of the following architectures or have experience with the models mentioned: DCNV2, MMOE, Deep & Wide, ESMM, xDeepFM, and GDCN