Senior Machine Learning Engineer
Hybrid
Posted 8d ago
Job Location
Hybrid
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
We are seeking a visionary and hands-on Lead Machine Learning Engineer to join our Austin team. In this role, you will be the principal architect behind the next generation of our unified AI/ML platform, designing and building the infrastructure that powers everything from traditional predictive models to generative AI, large language models (LLMs), and autonomous agent frameworks. You will own the end-to-end technical strategy, blueprint, and execution of scalable backend services and data pipelines that support AI-driven applications across go-to-market, engineering, and product teams. Because our products are initiated and owned entirely within the team, you will drive the vision from initial requirements and system design to global deployment, optimization, and long-term evolutionary ownership.
Responsibilities
- Architect and evolve a highly scalable, multi-tenant AI/ML platform unifying traditional ML and Generative AI/LLM orchestration.
- Design and implement production-grade AI Agents and Advanced Chatbots, including state management and memory architectures for Multi-Agent Systems.
- Build high-throughput, low-latency application backends and orchestration layers, partnering with other engineering teams for seamless feature delivery.
- Serve as a technical anchor for the Data Science team, enforcing engineering standards, leading reviews, and mapping business requirements to technical designs.
- Evaluate and drive adoption of modern AI infrastructure tools, embedding pipelines, vector databases, and serverless compute paradigms.
Requirements
- Extensive experience as a Senior or Lead ML Engineer with a proven track record of architecting and operating production-grade ML platforms and distributed backends.
- Strong competency in Traditional ML lifecycles (feature stores, training pipelines, model monitoring).
- Deep experience in Generative AI patterns including RAG pipelines, context engineering, fine-tuning, guardrailing, and agentic AI systems.
- Mastery of Python and robust experience with modern backend ecosystems.
- Familiarity with full-stack technologies like React and TypeScript is highly valued.
- A builder's mindset, comfortable navigating ambiguity, shaping technical roadmaps, and taking ownership of system reliability, costs, and model performance.
- 3+ years of dedicated ML Engineering experience within a large-scale, enterprise environment handling petabyte-scale data.
- Proven ability to architect, scale, and secure reliable, highly observable distributed systems.
- Experience mentoring engineers, leading by example, and fostering technical excellence.