Applied AI Engineer

About the Role

We are hiring an AI Engineer to help build the technical foundation for Vida’s AI agent platform. This is a hands-on engineering role focused on applied AI systems, LLM-powered workflows, model evaluation, data infrastructure, and production reliability. You will help design and implement the systems that make agents more accurate, observable, configurable, and scalable. You will work across the full AI engineering stack: prompt engineering, retrieval-augmented generation, vector databases, model evaluation, data pipelines, model monitoring, experiment tracking, and production ML infrastructure. You should be comfortable writing production software while also experimenting quickly with new models, tools, and techniques. In the near term, this is primarily an individual contributor role. You will partner closely with product, engineering, sales, customer-facing teams, and company leadership.

What You’ll Work On

• Vida’s product is powerful and technically complex. Your job will be to help make our agents more capable, reliable, measurable, and useful for businesses, resellers, and operators.
  • Examples of AI engineering problems you may work on include:
  • Building LLM-powered workflows for voice, messaging, computer-use, and business software automation.
  • Designing and improving retrieval-augmented generation systems, vector search, knowledge ingestion, and context management.
  • Creating evaluation systems that measure agent quality, accuracy, reliability, latency, and task completion.
  • Improving prompt engineering, tool use, agent orchestration, and guardrails for production agent behavior.
  • Building data pipelines that support experimentation, analytics, model improvement, and customer-facing insights.
  • Implementing model monitoring, experiment tracking, and feedback loops for continuous improvement.
  • Helping agents operate safely and reliably across complex, multi-step customer workflows.

What You’ll Do

• Applied AI and LLM Engineering
• Build and improve LLM-based systems using transformers, RAG, vector databases, prompt engineering, and evaluation frameworks.
• Design prompts, retrieval strategies, tool-use flows, and agent behaviors that work reliably in production.
• Prototype, test, and ship new AI capabilities for voice agents, messaging agents, computer-use agents, and workflow automation.
• Evaluate model performance using offline evaluation, human review, customer feedback, production telemetry, and experiment tracking.
• Translate ambiguous product requirements into practical AI system designs and production-ready implementations.
• Stay current with relevant AI techniques while applying strong judgment about what is ready for production.
• Machine Learning, Data, and Experimentation
• Build and maintain data pipelines, ETL workflows, data quality validation, and distributed processing systems.
• Use Python, SQL, PyTorch, scikit-learn, XGBoost, LightGBM, and related tools where they are the right fit.
• Use experiment tracking and evaluation workflows to compare models, prompts, datasets, and system changes.
• Partner with product and engineering to define the right metrics for agent quality, customer impact, and operational reliability.
• Improve data availability, labeling, validation, and feedback loops that support better agent performance over time.
• MLOps, Cloud, and Production Infrastructure
• Build and operate production AI and ML systems using Docker, Kubernetes, CI/CD, MLflow, Weights & Biases, feature stores, and model monitoring.
• Deploy, monitor, and scale AI services on AWS using infrastructure practices such as Terraform and Kubernetes.
• Improve reliability, observability, testing, and operational controls for AI systems in production.
• Partner with engineering to ensure AI capabilities are secure, maintainable, cost-aware, and scalable.
• Create tooling and infrastructure that helps the team ship AI improvements faster without sacrificing quality.

What We’re Looking For

• Strong software engineering skills, especially in JavaScript, Python, and SQL.
• Practical experience building applied AI, machine learning, or LLM-powered systems.
• Experience with PyTorch, scikit-learn, XGBoost, LightGBM, or similar ML libraries and frameworks.
• Experience with the modern LLM stack, including transformers, RAG, vector databases, prompt engineering, and evaluation.
• Experience building or operating production ML or AI systems using Docker, Kubernetes, CI/CD, MLflow, Weights & Biases, feature stores, or model monitoring.
• Experience with cloud and infrastructure platforms, especially AWS, Terraform, and Kubernetes.
• Experience building ETL pipelines, data quality validation, distributed processing systems such as Spark, and experiment tracking workflows.

Why Join Vida

• Build foundational software for AI agents and AI workforces.
• Work on one of the most important shifts in software: AI agents that can communicate, use tools, operate across systems, and complete real business work.
• Owning meaningful parts of the applied AI stack at a company where AI capability, reliability, and product quality directly shape the category.
• Partner closely with product, engineering, founders, sales, and leadership on real customer problems.
• Build for real businesses, real workflows, and real operational impact.
• Help shape how the AI workforce category is built, measured, and trusted by customers.
• Compensation: The expected base salary range for this role is 160,000 to 180,000, depending on experience, location, technical depth, and scope fit. In addition to salary, this role includes meaningful equity participation and standard company benefits.

How to Apply

Please send your resume to the link provided.