Staff Machine Learning Engineer

About this role

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Skills you'll use

What you'll do

• Drive High-Impact ML Systems Lead end-to-end development of models for ETA, risk, anomaly, and fraud—leveraging advanced techniques (embeddings, transformers, hybrid models).
• Build Data Science as a Platform Develop reusable ML infrastructure (features, experimentation, deployment, monitoring) to scale model development and reduce time-to-production.
• Lead GenAI & Agentic Systems Build LLM-powered solutions (RAG, diagnostics, automation, coding agents) and establish guardrails, evaluation, and explainability.
• Translate Business Problems into ML Solutions Convert customer workflows into well-defined ML problems and ensure measurable business impact.
• Drive Experimentation & Evaluation Establish strong offline/online evaluation frameworks tied to business outcomes.
• Collaborate Across Engineering Partner with MLE and DE to build scalable, reliable systems across the ML lifecycle.
• What We’re Looking For
• Experience 8–10+ years in Data Science / Applied ML with a strong track record of building and deploying production-grade ML systems.
• Core Modeling & Technical Expertise Deep expertise across tree-based models, transformers, probabilistic modeling, and feature engineering, with a strong data-centric mindset.
• Data & Platform Fluency Proficient in SQL and Python, with hands-on experience in modern data platforms (Snowflake/Databricks), pipelines (Spark, Airflow), streaming systems (Kafka), and MLOps tooling.

Who can apply

• Collaborate Across Engineering Partner with MLE and DE to build scalable, reliable systems across the ML lifecycle.
• Experience 8–10+ years in Data Science / Applied ML with a strong track record of building and deploying production-grade ML systems.
• Core Modeling & Technical Expertise Deep expertise across tree-based models, transformers, probabilistic modeling, and feature engineering, with a strong data-centric mindset.
• Data & Platform Fluency Proficient in SQL and Python, with hands-on experience in modern data platforms (Snowflake/Databricks), pipelines (Spark, Airflow), streaming systems (Kafka), and MLOps tooling.
• GenAI & LLM Capability Experience building RAG systems, working with embeddings and vector databases, and developing LLM-based applications and agentic workflows. Strong understanding of evaluation, guardrails, and safe deployment of GenAI systems.
• Systems & Engineering Mindset Familiarity with distributed systems, APIs, and deployment patterns, with the ability to write clean, production-quality code.
• Analytical Rigor & Diagnostics Strong ability to evaluate model performance, detect edge cases, run root cause analysis, and design robust monitoring and evaluation frameworks.
• Data Quality & Signal Awareness Experience handling messy, real-world data, including drift, bias, missingness, and anomalies, along with tools and techniques to detect and address these issues.

About project44

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