FoodingreeAbout this role
We are seeking an experienced Senior Machine Learning Engineer to design, develop, and deploy advanced AI systems that power intelligent applications and personalized recommendation experiences.
In this role, you will work across machine learning, computer vision, large language models (LLMs), AI agents, and full-stack software engineering. You will take ownership of designing intelligent algorithms, building agentic systems, optimizing recommendation engines, and translating emerging AI technologies into reliable, scalable production applications.
We are looking for a versatile engineer who combines strong machine learning fundamentals with excellent software engineering capabilities. The ideal candidate can independently identify complex technical challenges, develop innovative solutions, evaluate model performance, and deliver end-to-end AI-powered products.
Core Responsibilities
Machine Learning & AI Development: Design, develop, and optimize machine learning algorithms and intelligent systems for real-world applications, including computer vision and personalized recommendation systems.
AI Agents & Orchestration: Architect and build sophisticated AI agent systems, including multi-agent orchestration, tool calling, workflow automation, RAG, and LLM-powered applications.
Recommendation Systems: Develop and optimize recommendation and ranking algorithms to improve personalization, relevance, and user experience across our platform.
Model Evaluation & Optimization: Establish comprehensive model evaluation frameworks, automated benchmarks, regression testing, and performance metrics. Continuously improve model accuracy, reliability, latency, and computational efficiency.
AI Grounding & Retrieval Systems: Build reliable grounding systems that connect AI agent reasoning with verified data sources. Design and optimize retrieval pipelines, hybrid search, and structured data retrieval to improve response accuracy and reduce hallucinations.
Data & Evaluation Infrastructure: Develop data processing and representation strategies, establish quality metrics and monitoring dashboards, and build feedback loops that continuously improve model performance through user interactions and evaluation datasets.
Full-Stack Software Engineering: Design and develop frontend and backend components, APIs, databases, and application architectures to integrate AI capabilities into production web applications.
Production Deployment & Infrastructure: Deploy, maintain, and optimize AI models and software services. Ensure system reliability, scalability, observability, and efficient resource utilization.
Applied AI Research: Investigate emerging AI technologies, evaluate competing approaches, and translate validated research findings into practical production solutions.
Qualifications
Bachelor's or Master's degree in Computer Science, Machine Learning, Engineering, or a related technical field.
5+ years of professional experience in machine learning, software engineering, or applied AI, with demonstrated ownership of production systems.
Strong programming skills in Python and experience developing maintainable, production-quality software.
Deep understanding of machine learning and deep learning fundamentals, including model training, optimization, evaluation, and generalization.
Hands-on experience with PyTorch, TensorFlow, or equivalent deep learning frameworks.
Strong experience with computer vision and practical machine learning applications.
Demonstrated experience designing and building LLM-powered applications and AI agent systems.
Strong understanding of agent orchestration, tool integration, RAG, retrieval strategies, and LLM application architectures.
Proven ability to design model evaluation methodologies, construct high-quality evaluation datasets, establish benchmarks, and analyze model performance.
Strong software engineering fundamentals, including data structures, algorithms, API design, database systems, testing, and debugging.
Experience with full-stack or backend development and deploying applications in production environments.
Familiarity with cloud infrastructure, Docker, Linux, distributed systems, and GPU-based inference.
Preferred Qualifications
Experience developing multi-agent systems and complex agentic workflows.
Experience with advanced retrieval architectures, hybrid search, structured query generation, and data grounding.
Familiarity with Hugging Face, LangChain, LlamaIndex, or similar AI development frameworks.
Experience with React, TypeScript, FastAPI, or comparable full-stack technologies.
Experience with AWS, GCP, model serving, and scalable inference infrastructure.
Experience building automated evaluation pipelines, model monitoring dashboards, and continuous improvement workflows.
Experience integrating multimodal AI systems into production applications.
What We Are Looking For
We are looking for an engineer who can bridge the gap between advanced machine learning research and practical software engineering.
You should be comfortable taking ownership of the entire development lifecycle: identifying problems, designing algorithms, building AI systems, evaluating performance, and deploying reliable applications to production.
This is not a purely academic research position. We value engineers who can demonstrate strong technical judgment, independent problem-solving abilities, and a proven track record of delivering production-ready AI solutions.
If you are passionate about building intelligent products and transforming emerging AI capabilities into impactful real-world applications, we would love to hear from you.
Pay: Up to $180,000.00 per year
Benefits:
401(k)
401(k) matching
Dental insurance
Employee assistance program
Flexible schedule
Relocation assistance
Retirement plan
Tuition reimbursement
Education:
Bachelor's (Preferred)
Experience:
SDE industry: 5 years (Preferred)
Ability to Commute:
Santa Ana, CA 92708 (Preferred)
Work Location: Hybrid remote in Santa Ana, CA 92708
Company:
Foodingree
Level of experience (years):
Senior (5+ years of experience)
How to apply:
Please mention NLP People as a source when applying