Machine Learning Engineer

Wave; United States; Remote; USD 100,000 - 130,000; Full time

Benefits offered by this job

Bonus Structure

Employer-paid Benefits Plan

Health & Wellness Flex Account

Wellness Days

Paid Holiday Shutdown

Wave Days (extra vacation days in the夏

Job summary

Wave is seeking a Machine Learning Engineer in the United States to design, develop, and deploy foundational AI and ML models. You will build robust pipelines and platforms that support advanced analytics and business intelligence, ensuring ML systems are efficient, reliable, and deeply integrated into Wave's goals.

The role requires 3–5 years of hands-on ML experience with Databricks/Redshift, AWS SageMaker, Airflow, and end-to-end ML lifecycle tools.

Qualifications

• 3–5 years of professional ML engineering experience.
• Proven deployment of models to production environments.
• Experience with Databricks or Redshift.
• Hands-on with AWS SageMaker, Spark/AWS Glue, and IaC (Terraform).
• Experience with Airflow or similar orchestration systems.
• Experience with MLflow, Kubeflow, or SageMaker Feature Store.
• Familiarity with governance (lineage, fairness, privacy) and data cataloging.

Responsibilities

• Develop & Deploy: Hands-on building, training, and deployment of ML models in production.
• Champion Technical Standards: Advocate for coding, testing, and MLOps practices across pipelines.
• Optimize & Scale: Build scalable ML/AI use cases balancing existing models with new systems.
• Partner & Collaborate: Work with risk, product, and software teams to embed ML features.
• Establish Controls & Governance: Ensure model dependability, fairness, privacy; integrate lineage tracking and data protection workflows.
• Track & Evaluate: Build observability for model health and operational metrics.

Skills

ML Engineering

Production Deployment

Communication

FinTech domain

Tools

Databricks

Redshift

SageMaker

Airflow

MLflow

Kubeflow

Terraform

AWS Glue

Job description

At Wave, we help small businesses to thrive so the heart of our communities beats stronger. We work in an environment buzzing with creative energy and inspiration. No matter where you are or how you get the job done, you have what you need to be successful and connected. The mark of true success at Wave is the ability to be bold, learn quickly and share your knowledge generously.

As a Machine Learning Engineer, you will be a key contributor to the design, development, and deployment of our foundational AI and ML models. You will build robust, scalable machine learning pipelines and platforms that support advanced analytics and business intelligence. This role is perfect for an experienced person who wants to ensure our ML systems are efficient, reliable, and deeply integrated into our organizational goals.

Here is How You Make an Impact:

• Develop & Deploy: Focus on the hands-on building, training, and operational deployment of machine learning models, ensuring they perform reliably within existing production environments.
• Champion Technical Standards: Advocate for top-tier practices across coding, testing, and MLOps processes. Navigate ambiguity autonomously to refine pipelines and elevate ML engineering workflows.
• Optimize & Scale: Construct resilient, cost-efficient ML & AI use cases. Balance sustaining established models with accelerating the rollout of highly scalable, modern systems.
• Partner & Collaborate: Team up with cross-functional stakeholders, including risk specialists, product leads, and software developers, to convert strategic needs into technical specs and smoothly embed ML features into live applications.
• Establish Controls & Governance: Uphold stringent benchmarks for model dependability, fairness, and compliance. Direct the integration of lineage tracking and data protection workflows into our automated systems.
• Track & Evaluate: Formulate comprehensive observability systems to capture model health and key operational metrics, ensuring machine learning investments yield quantifiable organizational value.

You Thrive Here By Possessing the Following:

• Experience: Minimum of 3-5 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
• Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift.
• Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform.
• Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
• MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
• Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
• Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
• Industry Context: Experience in FinTech or Financial Risk environments is a significant advantage.

$100,000 - $130,000 a year

Final compensation is determined based on experience, expertise, and role alignment. Most candidates are hired within the middle of the range, with the upper end reserved for those bringing exceptional depth, impact, and immediate autonomy.

We also offer:

• Bonus Structure
• Employer-paid Benefits Plan
• Health & Wellness Flex Account
• Wellness Days
• Paid Holiday Shutdown
• Wave Days (extra vacation days in the summer)

At Wave, we value diversity of perspective. Your unique experience enriches our organization. We welcome applicants from all backgrounds. Let's talk about how you can thrive here!

Wave is committed to providing an inclusive and accessible candidate experience. If you require accommodations during the recruitment process, please let us know by emailing [e-mail]. We will work with you to meet your needs.

We use Google Gemini, a secure AI assistant, during interviews for note-taking purposes only. Notes are kept confidential and are not shared outside the hiring process. This allows our interviewers to stay fully focused on you during the conversation.

This advertised posting is a current vacancy.