Machine Learning Engineer (Agentic AI Platform)

Barker Staffing Solutions•Mountain View, CA

About The Position

We're building the next generation of agentic AI systems, intelligent, autonomous agents that reason, act, and continuously improve. As a Machine Learning Engineer, you won't just build models, you'll architect the entire ecosystem where our AI agents live, learn, and operate. This is a high-impact role for a product-minded, systems-level thinker who thrives in ambiguity and wants to shape foundational AI infrastructure from the ground up. You'll work at the intersection of LLMs, distributed systems, and real-world applications, owning everything from core ML architecture to customer-facing experiences.

Requirements

3–8 years of experience in Machine Learning Engineering or Software Engineering (ML-focused)
Strong production experience with Python
Hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow)
Hands-on experience with LLMs, agentic frameworks (e.g., LangGraph), or RAG systems
Experience designing scalable ML systems (training + serving)
Master's or PhD in Computer Science (or related field), OR Bachelor's degree + strong professional experience in software/ML engineering

Nice To Haves

Experience at top-tier tech companies (e.g., Meta, Google, Reddit, Pinterest)
Combined experience across Big Tech + high-growth startup environments
Background in ads, search, recommendation systems, or large-scale ML platforms
Prior experience at a venture-backed startup
MLOps and infrastructure experience: Kubernetes, MLflow, model serving systems
Data engineering experience: Spark, Airflow, dbt, ETL/streaming pipelines
Experience designing systems using lakehouse architectures

Responsibilities

Design and develop our core agentic AI platform, enabling autonomous reasoning, decision-making, and continuous learning
Implement multi-agent orchestration frameworks (e.g., LangGraph)
Architect a modern lakehouse-based data platform
Build scalable data pipelines, feature stores, and real-time ML serving systems
Build and optimize RAG systems, prompt pipelines, and reasoning workflows
Develop customer-facing applications, including a seamless AI chat interface
Create reliable, safe, and extensible tools that allow agents to interact with external systems, APIs, and data sources
Partner with data scientists to design infrastructure for training, fine-tuning, evaluation, and deployment
Implement robust experimentation, monitoring, and feedback loops
Write high-quality, scalable Python code
Ensure reliability, observability, and performance across distributed systems