Machine Learning Engineer II

About The Role

We’re looking for a skilled and driven Machine Learning Engineer to join our AI/ML team. In this role, you’ll work at the intersection of engineering, data science, and real-world impact—partnering directly with clients and internal stakeholders to design and deploy ML-powered tools that solve meaningful problems.

This role combines hands-on model development with robust backend engineering and infrastructure work. You’ll help build scalable systems, support R&D initiatives, and ensure rapid iteration and deployment of machine learning solutions in dynamic, production-ready environments.

What You’ll Do

As part of the Forward Deployed Engineering team, you’ll contribute during a phase of rapid growth as we focus on scaling models and improving platform performance. You’ll help build backend systems, support client-facing deployments, and enable smoother workflows for machine learning solutions.

• Develop and Maintain AI/ML Systems: Contribute to the development of reliable, scalable backend systems that power machine learning workflows and data pipelines.
• Cloud Operations and Deployment: Help manage and improve cloud infrastructure to support efficient model deployment and operational stability in real-world environments.
• Technical Problem Solving: Participate in identifying and addressing engineering challenges, including those surfaced through direct client feedback and usage.
• Collaborate Across Functions: Work closely with ML engineers, data scientists, and external stakeholders to integrate machine learning capabilities into production systems.
• Tooling and R&D Support: Assist in developing internal tools and infrastructure to streamline experimentation, training, and model serving across varied use cases.

What We Need

• Professional Experience:
  • 2+ years of experience in software engineering, ML engineering, or data-focused development roles.
  • Exposure to deploying machine learning models or supporting AI/ML workloads in production environments.
  • Experience in client-facing roles or comfort working with external stakeholders.
• Technical Expertise:
  • Proficient in Python, with experience building ML models or working with frameworks like PyTorch, TensorFlow, or similar.
  • Familiarity with building or supporting production-grade ML systems, such as RAG pipelines or agent-based applications, is a plus.
  • Solid understanding of core data science concepts, including statistical modeling, hypothesis testing, and data exploration techniques to inform model development and evaluation.
  • Experience with cloud platforms (AWS, GCP, Azure), and a solid grasp of deployment workflows and infrastructure best practices.
  • Familiarity with containerization tools (e.g., Docker, Kubernetes) is beneficial.
  • Ability to write clean, modular code and contribute to automated tests (unit, integration, and end-to-end).
  • Comfortable working with relational and/or NoSQL databases.
• ML Operations:
  • Familiarity with MLOps concepts, including model tracking, monitoring, and versioning.
  • Understanding of how DevOps principles apply to ML model development and deployment.
• Strong communication skills and a collaborative mindset, with the ability to engage effectively across internal teams and external client environments.

What’s in it for you

Compensation:
Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.

• Tier 1: $128,000 - $151,000