Staff Applied Scientist - Machine Learning

Job Description

Job Description

Rokt is an ecommerce technology company with the mission of making every transaction more relevant. The Rokt ecommerce Network leverages proprietary machine learning 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.

The Role: As a Staff Applied Scientist in the Rokt Brain team, you will lead applied research, experimentation, and model development projects - harnessing a diversity of ML theories, concepts, and models to improve complex systems powering 10 billion+ transactions per year.

Growth: pathways to Principal Applied Scientist (IC Track) and Applied Science Manager/Director (People Leader track) per your preferences and abilities.

Pay: Target total compensation ranges from $470k - $615k, comprised of a fixed annual salary of $290k - $360k, plus employee equity plan grant. In addition, you will receive world-class employee benefits.

Responsibilities:

Technical

• Own the research, design, development, testing, deployment and maintenance of machine learning systems and services at Rokt.
• Optimise auction and decisioning logic, applying model predictions to maximise value across Rokt’s multi-sided marketplace
• Conduct applied ML research, prototype new modeling approaches, and rigorously test innovations.
• Evaluate and improve model performance, ensuring robustness, scalability, and interpretability.
• Translate complex business needs into practical ML solutions, collaborating with product and engineering teams.
• Stay ahead of emerging ML trends, contributing to knowledge sharing through tech talks, brown bags, and best practice evangelism.

Leadership

• Act as technical and project leader of a small team of specialists
• Provide leadership on complex technical issues,
• Set standards for technical excellence - from coding to architectural best practices.
• Work cross-functionally with engineering and product leadership to plan and manage delivery expectations of roadmap items.
• Actively monitor progress and intervene where required to mitigate risks and bottlenecks, managing expectations within and outside the team.

Requirements

• PhD or equivalent experience in Computer Science, Statistics, Mathematics, or related field with specialization in ML, AI, or Information Retrieval, and experience leading PhD level scientists/engineers on projects.
• 10+ years of industry experience building production-grade ML systems.

+ Deep expertise in at least two of the following:

• ML for Ads, E-commerce, or Two-Sided Marketplaces
• Deep Learning Architectures e.g MMoE, PLE, DCN, Transformers, Graph Neural Networks
• Bayesian Modelling & Probabilistic Methods
• Reinforcement Learning (Contextual Bandits, Policy Optimization)
• Price & Revenue Optimisation
• Representation Learning & Embeddings
• Model Optimisation (quantisation, distributed training, GPU optimisation, JIT compilation, mixed precision, inference optimisation)
• Knowledge Distillation
  

Hybrid work structure:
Rokt has a 4 day in-office, 1 day remote hybrid structure - with a flexible approach to start/finish times.

Benefits

• Equity in a profitable, fast-growing company approaching $1 Billion in revenue.
• Dollar-for-dollar 401K matching plan (up to 4% of fixed annual remuneration)
• Fully funded health insurance (Dental, Optical, and Medical)
• Generous allowances for wellness, technology, mobile, and transit.
• Daily catered lunch, stocked pantry & fridges
• Extra leave (bonus annual leave, sabbatical leave etc.)

Equal employment opportunities are available to all applicants without regard to race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

If this sounds like a role you’d enjoy, apply here, and you’ll hear from our recruiting team.

Note: The first stage of Rokt's recruitment process is a 15-minute online aptitude test, which will be sent out to your application email. Successful candidates will be contacted on next steps.