Machine Learning Engineer
US$157,000 – US$198,500 / yr base salary
Posted 25 Sep 2026
Advertised as “Senior Machine Learning Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3–5 yrs
- Management level
- Individual contributor
| Base salary | US$157,000 – US$198,500 / yr |
|---|
CertificationsProfessional Machine Learning Engineer, Professional Cloud Architect, Generative AI Leader
Are you looking to Optimize your life? Start your exciting path to a rewarding career today!
We are Optimum, a leader in the fast-paced world of connectivity, and we're seeking driven and enthusiastic professionals to join our team, empower lives, fuel businesses, and drive innovation. Connectivity is no longer a luxury, but a necessity. A career at Optimum means you'll be enabling progress and enhancing lives by providing reliable, high-speed connectivity solutions that keep the world connected. Our successes, now and in the future, are powered by our amazing product, a commitment to our people and culture, and the connections we make in our communities.
If you are resourceful, collaborative, and passionate about delivering consistent excellence, Optimum is for you!
Job Summary
Machine Learning Engineers work to deploy end-to-end solutions to business problems leveraging AI and/or ML principles as needed to create those solutions. MLEs will take requests from stakeholders, define the components required for the project, gather data necessary for project EDA and training, then work with stakeholders to develop a plan around the productionized use of the solution, and work to put that solution into final production.
Responsibilities
- Consult with stakeholders to gather business requirements, translate them into agentic AI and data solutions, design high-level agent and model architectures, and demonstrate deep expertise in advanced analytics, LLMs, and AI/ML techniques to design, prototype, and build production-grade solutions to business problems.
- Architect, build, and deploy agentic AI systems (single-agent and multi-agent workflows) on Google Cloud, leveraging Google's Customer Engagement Suite (CES), Vertex AI Agent Builder, and Gemini-family models to automate enterprise workflows in customer engagement, sales, marketing, and operations.
- Design, integrate, and orchestrate the tools, APIs, function calls, retrieval pipelines (RAG), memory stores, and guardrails that extend agent capabilities, and own the end-to-end deployment, observability, evaluation, and lifecycle management of these agents in production.
- Lead communication with other stakeholders to drive agentic use case development and manage expectations on model and agent limitations, latency, cost, and lead times.
- Analyze data to identify useful relations, patterns and features that are predictive of user behaviors, preferences, intents, and interests, and use these signals to ground and personalize agent behavior.
- Manage and execute entire projects from start to finish, including cross-functional project management; data collection and manipulation, analysis and modeling; communication of insights and recommendations; productionalization of final model and agent products.