Staff Machine Learning Engineer
ROLE OVERVIEW
We are seeking a visionary and hands-on Staff Machine Learning Engineer to lead the technical strategy, architecture, and execution of our Growth and Engagement ML initiatives. In this role, you will bridge the gap between advanced machine learning and business strategy, designing systems that drive user acquisition, retention, lifetime value (LTV), and deep product engagement.
As a technical pillar of the engineering organization, you will own the end-to-end lifecycle of complex ML models, mentor senior engineers, and collaborate closely with Product, Data Science, and Marketing leadership to move core business metrics.
Key Responsibilities
Technical Leadership & Strategy
- Define the long-term technical roadmap for Growth and Engagement ML systems, ensuring scalability, reliability, and measurable business impact.
- Architect and deploy production-grade ML pipelines and real-time decisioning systems that power personalization, notification dispatch, and onboarding flows.
- Evaluate and integrate cutting-edge ML techniques, including multi-armed bandits, reinforcement learning, LLMs for content generation, and advanced graph neural networks.
Execution & Modeling
- Design, train, and validate sophisticated models targeting user lifecycle stages: propensity to churn, lifetime value (LTV) forecasting, next-best-action, and lookalike modeling.
- Build and optimize recommendation engines and semantic search systems to surface highly relevant content, products, or features to users.
- Establish robust experimentation frameworks (advanced A/B testing, causal inference, and multi-variate testing) to rigorously validate model variants in production.
Collaboration & Mentorship
- Partner with Product and Growth marketing teams to translate high-level business hypotheses into precise, actionable machine learning problems.
- Mentor and coach senior engineers across the data and ML organizations, fostering a culture of technical excellence and continuous learning.
- Advocate for ML engineering best practices, including model monitoring, feature store utilization, reproducible training pipelines, and data governance.
Qualifications & Skills
Experience
- 8+ years of professional experience in machine learning engineering, data science, or software engineering, with at least 3+ years in a Staff, Principal, or Tech Lead capacity.
- Proven track record of building and scaling ML systems specifically within growth, marketing tech, recommendation engines, or consumer engagement domains.
- Extensive experience with large-scale data processing and distributed computing.
Technical Proficiencies
- Languages: Expert-level Python, Scala, or Java.
- ML Frameworks: PyTorch, TensorFlow, JAX, or XGBoost.
- Data & MLOps Infrastructure: Spark, Flink, Kafka, Snowflake/BigQuery, Ray, Kubeflow, MLflow, or SageMaker.
- Experimentation: Deep understanding of causal inference, uplift modeling, and robust statistical testing methodologies.
Core Competencies
- Business Acumen: Ability to directly connect algorithmic improvements to top-line growth metrics (e.g., MAU/DAU, conversion rates, retention curves).
- Communication: Exceptional ability to explain highly complex technical architectures and algorithmic choices to non-technical stakeholders and executives.
WHY WORK WITH US
OPPORTUNITY
We are a team of experienced builders with a ton of traction in a big and growing market – our users are so passionate they were hacking their way into our private beta. We've acquired millions of users in our 3+ years and are adding more every week! On top of that, there has never been a better time to work in crypto and on wallets in particular.
- Wallets play a pivotal role: Wallets are responsible for on-boarding new users into crypto, and can make or break the user experience.
- We are moving to a multi-chain world: New blockchains and scaling solutions are coming online and gaining traction, but are lacking decent wallets and bridges.
- DeFi & NFTs are exploding : Interest in DeFi and NFTs has exploded, yet they are still an after-thought in existing wallets.
BENEFITS
- Competitive salary and equity
- Comprehensive insurance (medical/dental/vision) 100% covered
- Stipend for your ideal remote / WFH set-up: laptop, headphones, and any other work gear you may need
- Flexible hours and a long-standing, supportive remote environment
- Unlimited vacation: Take time when you need it (and we really mean it!)
- 401(k) retirement plan
- Wellness benefit
- Daily lunch benefit