Principal Applied Machine Learning Engineer

 

About the Role:

We are seeking a Principal Applied Machine Learning Engineer to be the foundational hire responsible for establishing Boost’s machine learning capabilities. This is a high-impact, high-ownership role for someone who thrives in greenfield environments and is excited to design, build, and scale ML systems from scratch.

• You will lead the development of ML infrastructure and pipelines, work on key use cases across operations and product, and drive best practices for MLOps, model deployment, and lifecycle management.
• As a deeply technical and execution-focused individual, you will collaborate closely with business, engineering, and data stakeholders to translate strategic opportunities into deployable machine learning solutions. You'll also play a pivotal role in mentoring team members, embedding ML thinking across the organization, and evangelizing AI/ML adoption.

Key Responsibilities:

• • Design, build, and own end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, deployment, and automated retraining.
  • Develop models for classification, regression, NLP, and LLM-based use cases, aligned to critical business needs in operations, payments, and product workflows.
  • Establish and maintain ML infrastructure, including CI/CD workflows for ML, model versioning, monitoring, and automated deployment.
  • Leverage AWS services—including SageMaker, Bedrock, Lambda, Comprehend, and Rekognition—to develop secure, scalable, and cost-effective ML solutions.
  • Set and implement best practices for the entire ML lifecycle, using tools like MLflow, SageMaker Pipelines, and feature stores to ensure experiment reproducibility, traceability, and governance.
  • Translate business requirements into technical designs in close collaboration with product, data, and engineering teams.
  • Define success metrics for ML initiatives, scope MVPs, and iterate based on feedback and performance.
  • Act as a catalyst for AI/ML capability-building by mentoring team members, sharing best practices, and embedding ML literacy across the organization.
  • Stay current with emerging ML trends and AWS innovations and assess their potential for business application at Boost.
  • Integrate ML models into production environments, working closely with backend engineers to ensure seamless deployment into microservices or data pipelines.
  • Own the delivery of ML projects from ideation to monitoring, operating with autonomy and a strong bias for action.

Qualifications:

• • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related technical field.
  • 5+ years of experience in a Machine Learning Engineering or Applied ML role, with demonstrated impact in production environments.
  • Deep hands-on experience with AWS ML services, especially SageMaker and Bedrock.
  • Strong programming skills in Python (preferred), with additional experience in Java or Scala.
  • Expertise in traditional ML algorithms (e.g., XGBoost, Random Forests) as well as experience working with LLMs or foundation models.
  • Demonstrated experience designing and deploying ML infrastructure and pipelines in cloud environments.
  • Applied experience with MLflow or similar platforms for tracking experiments and managing models.
  • Solid understanding of model evaluation, feature engineering, hyperparameter tuning, and practical deployment constraints.
  • Strong interpersonal skills and ability to collaborate with cross-functional stakeholders to scope and deliver business-aligned ML solutions.
  • Self-starter with a strong sense of ownership and the ability to thrive in a greenfield environment.
  • Experience mentoring junior engineers or data scientists and fostering a collaborative, growth-oriented culture.

Preferred Qualifications:

• • AWS Machine Learning Specialty certification or equivalent.
  • Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
  • Familiarity with big data tools like Spark, Kafka, or Hadoop.
  • Understanding of MLOps principles, including model monitoring, drift detection, and CI/CD for ML.
  • Exposure to fintech, payments, or regulated environments is a plus.

Job Type: Full-time, Hybrid

Compensation: $80k - $95k annually

 

We’re an equal opportunity employer (EOE) that empowers our people. It is the policy of ELEVI to provide equal employmen