Machine Learning Engineer Job at Barker Staffing Solutions LLC in Mountain View
Barker Staffing Solutions LLC, Mountain View, CA, United States
About the Role
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.
What You'll Do
Architect & Build Agentic Systems
Design and develop our core agentic AI platform, enabling autonomous reasoning, decision-making, and continuous learning
Implement multi-agent orchestration frameworks (e.g., LangGraph)
Own the ML & Data Infrastructure
Architect a modern lakehouse-based data platform
Build scalable data pipelines, feature stores, and real-time ML serving systems
Develop LLM-Powered Applications
Build and optimize RAG systems , prompt pipelines, and reasoning workflows
Develop customer-facing applications, including a seamless AI chat interface
Build Tool Machines for Agents
Create reliable, safe, and extensible tools that allow agents to interact with external systems, APIs, and data sources
Drive MLOps & Model Lifecycle
Partner with data scientists to design infrastructure for training, fine-tuning, evaluation, and deployment
Implement robust experimentation, monitoring, and feedback loops
Ship Production-Grade Systems
Write high-quality, scalable Python code
Ensure reliability, observability, and performance across distributed systems
What We're Looking For
Core 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)
LLMs, agentic frameworks (e.g., LangGraph), or RAG systems
Experience designing scalable ML systems (training + serving)
Preferred Background
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
Nice to Have
MLOps and infrastructure experience:
Kubernetes, MLflow, model serving systems
Data engineering experience:
Spark, Airflow, dbt, ETL/streaming pipelines
Experience designing systems using lakehouse architectures
Education
Master's or PhD in Computer Science (or related field), OR
Bachelor's degree + strong professional experience in software/ML engineering
Tech Stack
Languages & Frameworks: Python, PyTorch, TensorFlow
AI/LLM: LangGraph, RAG architectures
Infrastructure: Kubernetes, MLflow
Data: Spark, Airflow, dbt, lakehouse architecture
Who You Are
Product-minded: You think about user experience, not just models
Systems thinker: You design for scale, reliability, and extensibility
Builder: You ship fast, iterate quickly, and thrive in ambiguity
Impact-driven: You want to own and shape foundational technology
What Success Looks Like
You've built scalable systems powering autonomous AI agents in production
You've improved model performance and reliability through robust infrastructure and feedback loops
You've delivered end-to-end ML products used by real customers
Why Join Us
Build cutting-edge agentic AI systems from the ground up
Own foundational architecture across the entire AI stack
Work alongside a team operating at the intersection of LLMs, infrastructure, and product
Massive opportunity for ownership, impact, and growth
#J-18808-Ljbffr
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.
What You'll Do
Architect & Build Agentic Systems
Design and develop our core agentic AI platform, enabling autonomous reasoning, decision-making, and continuous learning
Implement multi-agent orchestration frameworks (e.g., LangGraph)
Own the ML & Data Infrastructure
Architect a modern lakehouse-based data platform
Build scalable data pipelines, feature stores, and real-time ML serving systems
Develop LLM-Powered Applications
Build and optimize RAG systems , prompt pipelines, and reasoning workflows
Develop customer-facing applications, including a seamless AI chat interface
Build Tool Machines for Agents
Create reliable, safe, and extensible tools that allow agents to interact with external systems, APIs, and data sources
Drive MLOps & Model Lifecycle
Partner with data scientists to design infrastructure for training, fine-tuning, evaluation, and deployment
Implement robust experimentation, monitoring, and feedback loops
Ship Production-Grade Systems
Write high-quality, scalable Python code
Ensure reliability, observability, and performance across distributed systems
What We're Looking For
Core 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)
LLMs, agentic frameworks (e.g., LangGraph), or RAG systems
Experience designing scalable ML systems (training + serving)
Preferred Background
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
Nice to Have
MLOps and infrastructure experience:
Kubernetes, MLflow, model serving systems
Data engineering experience:
Spark, Airflow, dbt, ETL/streaming pipelines
Experience designing systems using lakehouse architectures
Education
Master's or PhD in Computer Science (or related field), OR
Bachelor's degree + strong professional experience in software/ML engineering
Tech Stack
Languages & Frameworks: Python, PyTorch, TensorFlow
AI/LLM: LangGraph, RAG architectures
Infrastructure: Kubernetes, MLflow
Data: Spark, Airflow, dbt, lakehouse architecture
Who You Are
Product-minded: You think about user experience, not just models
Systems thinker: You design for scale, reliability, and extensibility
Builder: You ship fast, iterate quickly, and thrive in ambiguity
Impact-driven: You want to own and shape foundational technology
What Success Looks Like
You've built scalable systems powering autonomous AI agents in production
You've improved model performance and reliability through robust infrastructure and feedback loops
You've delivered end-to-end ML products used by real customers
Why Join Us
Build cutting-edge agentic AI systems from the ground up
Own foundational architecture across the entire AI stack
Work alongside a team operating at the intersection of LLMs, infrastructure, and product
Massive opportunity for ownership, impact, and growth
#J-18808-Ljbffr