About Vida
Vida is a publicly traded, early-stage AI company building an AI Agent Operating System that enables businesses and partners to create, deliver, and manage AI workforces. Our platform powers AI agents that communicate, automate workflows, connect with business software, and help teams operate more efficiently across channels and systems.
We are at an important stage of product and company. The core platform is expanding across voice, messaging, computer-use agents, reseller tooling, integrations, analytics, and reliability, security, and operational controls. As the product expands in scope and complexity, AI engineering will play a critical role in making agents more capable, reliable, measurable, and useful across real customer workflows.
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 should be excited to work directly on core systems, collaborate closely with product and engineering, and help turn emerging AI capabilities into reliable customer-facing product experiences.
You will partner closely with product, engineering, sales, customer-facing teams, and company leadership.
This is an ideal role for an engineer who wants broad ownership, high leverage, and the opportunity to build applied AI systems that power real business operations at scale.
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.
- Work with 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.
- Help 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.
- Ability to move between research, prototyping, engineering implementation, and production operations.
- Strong judgment about tradeoffs between model quality, latency, cost, reliability, safety, and customer experience.
- Comfort working from ambiguity and turning early ideas into shipped product capabilities.
- Strong communication skills and ability to explain technical decisions clearly to product, engineering, and leadership.
- High ownership, curiosity, and a bias toward shipping.
Nice to Have
- Experience building AI agents, workflow automation, voice agents, conversational AI, customer support automation, telephony, CRM, or developer tools.
- Experience with agent evaluation, tool use, function calling, computer-use agents, or multi-step AI workflows.
- Experience building retrieval, ranking, embedding, or knowledge ingestion systems at production scale.
- Experience with real-time systems, voice infrastructure, latency-sensitive applications, or observability for distributed systems.
- Experience working in an early-stage company or high-ambiguity product environment.
- Experience with security, privacy, compliance, or regulated customer environments.
What Success Looks Like
In your first few months, you will help Vida improve the reliability, measurability, and production quality of our AI systems while shipping practical improvements to agent capabilities.
You will make agents easier to evaluate, easier to monitor, and more reliable across customer workflows.
Over time, you will become a key technical owner for Vida’s applied AI systems, helping define how we build, evaluate, deploy, and scale AI agents that communicate, operate, and complete real business work.
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.
- Own 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 160,000-180,000, depending on experience, location, technical depth, and scope fit.
In addition to salary, this role includes meaningful equity participation and standard company benefits. We view this as a high-leverage role with broad product and company impact.
How to Apply
Please send your resume to the link provided.