Senior Machine Learning Engineer

Remote (United States)

About the Role

This opportunity is for a Senior Machine Learning Engineer focused on building, productionizing, and improving machine learning models that support marketing-focused products. The role combines model development, scalable ML infrastructure, experimentation, and cross-functional collaboration across product and engineering teams.

This position is well suited for a versatile engineer who can own problems end-to-end, move across data preparation, model development, deployment, monitoring, and performance optimization, and communicate complex machine learning concepts clearly to both technical and non-technical stakeholders.

Status: Full-Time

Compensation:$180,000 – $250,000 per year, plus equity

What You’ll Do

• Design, train, deploy, and productionize machine learning models that directly support marketing-focused product use cases.
• Build models that help determine which customers or prospects should receive messaging, when they should receive it, and through which channel.
• Develop and maintain scalable MLOps pipelines for reliable model training, serving, deployment, and monitoring in production.
• Architect robust machine learning infrastructure that can support production-scale systems.
• Run experiments and continuously improve models using A/B testing, uplift modeling, causal inference, and other advanced experimentation frameworks.
• Validate and refine model performance through structured experimentation and performance measurement.
• Collaborate closely with cross-functional teams, including executive and technical leadership, to align machine learning work with product goals.
• Help establish and promote best practices for machine learning and data engineering across the organization.
• Mentor and support team members through strong technical collaboration and shared ownership.

Qualifications

• 5+ years of full-time software engineering experience, including at least 3 years working on machine learning systems.
• Deep knowledge of modern machine learning algorithms, including tree-based methods, deep learning architectures, transformers, and large language models.
• Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, XGBoost, or equivalent tools.
• Experience with feature engineering using aggregations, embeddings, and sub-models.
• Track record of building production-scale machine learning infrastructure, ideally with tools such as GCP, Vertex AI, Kubeflow, or BigQuery.
• Familiarity with CI/CD, containerization tools such as Docker and Kubernetes, and distributed training technologies such as Spark, Ray, or Dask.
• Experience iterating machine learning models in a production environment.
• Strong proficiency in Python and common data tools such as NumPy and pandas.
• Experience with scalable data processing technologies such as Spark, Ray, or BigQuery.
• Experience with job orchestration tools such as Airflow.
• Comfort working with advanced experimentation techniques.
• Understanding of performance measurement in real-world model deployments.
• Ability to work across the full machine learning lifecycle, including data wrangling, model development, deployment, monitoring, and optimization.
• Excellent communication skills, including the ability to explain complex machine learning concepts to non-technical stakeholders.
• Self-starter mindset with the ability to own projects from ideation through deployment.
• Ability to learn and apply new technologies as needed.

Preferred Qualifications

• Familiarity with marketing technology or advertising technology.
• Experience with experimental design and methods such as causal inference or uplift modeling.
• Exposure to modeling with large language models and modern AI tooling.
• Experience productionizing reinforcement learning or bandit algorithms.
• Ph.D. in a technical field.
• Experience working in a fast-paced or startup environment.
• Living in or near New York City is a plus.
• Familiarity with Eastern Time collaboration is helpful, as many team members work in EST.