Senior Machine Learning Engineer

Role Overview

• Build scalable AI proof-of-concepts that are designed to demonstrate a clear path from prototype to enterprise-scale solution
• Partner with product managers, business stakeholders, platform teams, and AI & software engineers to translate ambiguous business needs into practical AI solutions with measurable value
• Design and implement modern AI systems using LLMs, agentic workflows, retrieval-augmented generation, data pipelines, APIs, cloud-native services, and enterprise platforms
• Rapidly validate feasibility and value by assessing technical risk, data readiness, integration complexity, user experience, security considerations, performance, cost, and business impact
• Create reusable technical assets such as reference architectures, reusable components, documentation, decision records, and handoff materials that enable product, platform, or delivery teams to scale successful POCs
• Raise the technical bar for the team by modeling strong engineering practices, mentoring others, improving delivery patterns, and bringing clear ownership and accountability to uncertain, fast-moving work.

Requirements

• Doctorate degree OR Master’s degree and 2 years of relevant experience OR Bachelor’s degree and 4 years of relevant experience OR Associate’s degree and 8 years of relevant experience OR High school diploma / GED and 10 years of relevant experience
• 4-6 years of relevant experience in AI engineering, machine learning engineering, software engineering, data engineering, cloud engineering, or related technical roles
• Demonstrated experience building full-stack AI-powered applications that move beyond experimentation and are designed with scalability, security, evaluation, and maintainability in mind
• Strong understanding of modern AI application architecture, including LLMs, retrieval-augmented generation, embeddings, vector databases, agentic workflows, tool use, orchestration frameworks, and AI evaluation methods
• Experience defining and applying evaluation methods for AI solutions, including accuracy, reliability, hallucination risk, latency, usability, safety, cost, and fitness for intended use
• Ability to translate ambiguous business problems into practical technical approaches, make sound tradeoffs, and rapidly validate feasibility, value, risks, and path to scale
• Experience with AWS Cloud, data pipelines, integration architecture, containers, CI/CD, observability, and secure development practices
• Strong software engineering foundation, preferably with Python and modern development practices, including testing, version control, modular design, documentation, and maintainable code
• Demonstrated ability to responsibly use AI tools to improve engineering productivity, explore technical solutions, automate repetitive tasks, and enhance the delivery of machine learning or software products
• Excellent communication, ownership, and cross-functional leadership skills, with the ability to partner effectively with product managers, business stakeholders, platform teams, and AI and software engineers.

Tech Stack

• AWS
• Cloud
• Python

Benefits

• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
• group medical, dental and vision coverage
• life and disability insurance
• flexible spending accounts
• A discretionary annual bonus program
• Stock-based long-term incentives
• Award-winning time-off plans
• Flexible work models where possible.