22 days ago
Base Salary
$217k - $303k/yr
Responsibilities
- Design, build, deploy, and operate production-grade machine learning models and systems at scale.
- Own the ML lifecycle from problem definition and feature engineering through training, evaluation, deployment, monitoring, and automated retraining.
- Build scalable data pipelines, model pipelines, model-serving infrastructure, and real-time decision systems.
- Improve ranking, recommendations, search relevance, prediction, content and user understanding, and optimization systems using large-scale datasets.
- Improve latency, throughput, reliability, observability, and model quality metrics.
- Research and apply deep learning, graph-based methods, transformers, and LLM evaluation and alignment techniques.
- Partner with Product, Data Science, Infrastructure, and Engineering teams to translate product and business problems into ML solutions.
- Contribute to technical strategy, architecture, and the long-term ML roadmap.
Requirements
- 3-5+ years of experience building, deploying, and operating machine learning systems in production.
- Strong programming skills in Python, Java, Go, or similar languages, along with solid software engineering fundamentals.
- Strong understanding of machine learning algorithms spanning statistical learning, XGBoost, Random Forests, regressions, Transformers, CNNs, and GNNs.
- Hands-on experience with modern ML frameworks such as PyTorch or TensorFlow.
- Experience designing scalable ML pipelines, data processing systems, and model-serving infrastructure.
- Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions.
- Experience improving measurable metrics through applied machine learning.
- Preferred experience with recommender systems, search or ranking systems, advertising or auction systems, representation learning, or multimodal embedding systems.
- Preferred familiarity with distributed systems and large-scale data processing using Spark, Kafka, Ray, Airflow, BigQuery, or Redis.
- Preferred experience with real-time, low-latency production environments, feature engineering, model optimization, and production monitoring.
- Preferred experience with LLM or generative AI techniques such as evaluation, alignment, fine-tuning, knowledge distillation, RAG or agentic systems, and productionizing LLM-powered products.
- An advanced degree in Computer Science, Machine Learning, or a related quantitative field is preferred.
Benefits
- Comprehensive healthcare benefits and income replacement programs.
- 401(k) with employer match.
- Global benefits supporting workspace, professional development, caregiving, and lifestyle needs.
- Family planning support and gender-affirming care.
- Mental health and coaching benefits.
- Flexible vacation and paid volunteer time off.
- Generous paid parental leave.
- U.S.-based employees may receive medical, dental, and vision insurance and other benefits.
- The role may include equity through restricted stock units and, depending on the position, commission.
Tech Stack
Categories
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