Senior Machine Learning Engineer

• Practice Data

• Technologies Business Intelligence Jobs and Data Recruitment

• Location Long Island City, United States

• Salary NON 200,000 Per Year

• Type Permanent

We’re partnering with a fast-growing, data-driven organization that’s looking to bring on a Machine Learning Engineer to join a highly collaborative and innovative team. This is a key role where you’ll drive the development of predictive models that directly influence product performance and client outcomes.

What you’ll be doing:

• Lead the design, development, and optimization of predictive machine learning models
• Analyze large, complex datasets to uncover trends and actionable insights
• Partner with data engineering teams to build and maintain feature pipelines
• Evaluate and iterate on model performance using robust testing methodologies
• Deploy models into production environments and integrate via APIs
• Collaborate cross-functionally with product, engineering, and technical stakeholders
• Stay at the forefront of ML and data science advancements, bringing new ideas into practice

What we’re looking for:

• Advanced degree (Master’s or PhD) in a relevant field
• Strong experience building and deploying ML models in a production environment
• Deep understanding of machine learning techniques (e.g. regression, classification, clustering, deep learning)
• Proficiency in Python and common ML libraries (TensorFlow, PyTorch, scikit-learn)
• Experience working with SQL, cloud platforms (AWS, GCP, or Azure), and modern data warehouses
• Strong communication skills with the ability to work across technical and non-technical teams
• A proactive mindset with a passion for solving complex problems using data

Tech stack & tools:
Python | SQL | Cloud Platforms (AWS, GCP, Azure) | Data Warehouses (Snowflake, BigQuery, Redshift) | AI/LLM APIs | Git

Nice to have:

• Experience with dbt, semantic layers, or modern data transformation tools
• Exposure to TypeScript
• Background in Bayesian modeling or marketing analytics (e.g. marketing mix modeling)
• Familiarity with experimentation frameworks (A/B testing, causal inference)
• Understanding of digital marketing ecosystems and attribution models

💡 This is an opportunity to work on high-visibility projects where your work will directly shape product strategy and business impact.

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