Senior Software Engineer, AI

Lattice

22h•Remote

About The Position

This is Engineering at Lattice. Lattice’s Engineering team is continuously improving both our product and our craft. We build maintainable, performant systems using modern technologies, and we collaborate closely with product and design to deliver exceptional user experiences. Our AI Engineering team is building the systems that power how AI works across Lattice. We’ve laid the foundations: traces are flowing and evals are running - and we’re now focused on defining how our AI products are measured, improved, and trusted at scale. This is a high-ownership role where you’ll help shape evaluation methodology, agent architecture, and the core systems that determine how AI performs in production.

Requirements

5+ years of professional software engineering experience with significant time spent on production AI/ML systems.
Deep hands-on experience with LLM-based systems: prompt engineering, RAG pipelines, agent orchestration, evaluation metrics, and model fine-tuning.
Proven ability to work with data and understand statistics, especially in experiments.
Proven ability to build and operate agentic AI systems in production: multi-step workflows, multi-agent topologies, and the failure modes that come with them.
Strong command of AI evaluation: you’ve built eval frameworks before, you know the difference between a good eval and a vanity metric, and you have opinions about it.
Production-grade Python engineering: clean, maintainable, testable code.
LangGraph or comparable agent orchestration frameworks. You’ve built real agent workflows with it, not just tutorials.
LangSmith or comparable LLM observability tooling for tracing, evaluation, and debugging.
Reads AI papers & blogs regularly and is a trusted source of AI trends.
Vector databases (Pinecone or similar) and retrieval system design.
AWS ecosystem or other cloud infrastructure (ex GCP). Comfortable with lambdas, queues, and cloud-native architecture.
Clear eyes: you see problems as they are, not as you’d like them to be. You surface hard truths early and address them directly.
Ship, shipmate, self: you prioritize the product and your teammates. Low ego, high ownership.
You’re as comfortable in ambiguity as you are in well-defined problems: early foundations mean you’ll encounter both.
Strong technical communication: you can debate evaluation methodology with an AI lead and explain it clearly to an EM in the same afternoon.

Nice To Haves

Experience with RLHF, LoRA, or other model adaptation techniques.
Background in traditional ML (supervised/unsupervised, neural networks) and knowing when an LLM is overkill.
Experience with MLOps tooling: MLflow, DataDog, CI/CD pipelines for model deployment.
Published work, conference talks, or open-source contributions in AI/ML.
Experience in HR tech, people analytics, or other domains where data quality and trust are critical.
Familiarity with TypeScript is a plus. Our full-stack engineers use it and cross-pollination is valuable.

Responsibilities

Design and ship a robust, end-to-end AI evaluation framework, covering offline evals, production tracing, and human-in-the-loop feedback loops, connected across all of Lattice’s AI use cases.
Define and instrument the metrics that actually matter: agent task completion, hallucination rates, response quality, user engagement, and downstream business outcomes.
Build and maintain evaluation datasets, test harnesses, and automated scoring pipelines to catch regressions before they ship.
Identify and surface the drivers of agent quality improvement, giving the team clear signals on where to invest.
Architect and implement reusable agent infrastructure: multi-turn conversation workflows, recommendation services, LLM DAGs, and standardized agent topology patterns using LangGraph.
Build and scale RAG pipelines and retrieval infrastructure, including vector store management and retrieval quality optimization.
Make principled build vs. buy decisions across LLM providers, agent frameworks, and evaluation tooling, balancing capability, cost, latency, and vendor risk.
Contribute to production AI systems with a strong focus on reliability, observability, and performance, not just prototypes.
Own projects end-to-end: scope them, drive them to completion, and bring in the right people at the right time.
Partner with engineering leads and managers to inform technical direction on agent quality and evaluation strategy you’ll be expected to hold intelligent, substantive conversations about methodology, not just implementation.
Raise the AI engineering bar across the broader team through code review, documentation, and thoughtful technical debate.