Senior AI/ML Engineer

Tempo Software

Senior AI/ML Engineer at Tempo working on LLMs and decision-making systems. Delivering production AI systems collaboratively with domain engineers and external partners.

Posted 6/3/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

About the role

Key responsibilities & impact

Signal detection and anomaly detection
Statistical and ML-based detectors that identify meaningful patterns in portfolio signals — velocity changes, capacity saturation, dependency risks — from CDC event streams and external tool integrations. Not simple threshold alerts; intelligent pattern recognition that knows the difference between noise and a real problem.
Insight synthesis engine
An LLM-powered correlation engine that takes raw signals and produces actionable insights with root causes, confidence scores, and evidence chains. Not just “something is wrong” — but “why it’s wrong, what it means, and what you should do about it.”
Planning Rules compiler
A translation layer between natural language planning rules (written by portfolio managers) and the structured parameters that drive our Monte Carlo scheduling engine. The LLM interprets intent; the deterministic engine computes schedules. You’ll design how these two layers communicate reliably.
Evaluation and testing frameworks
The pipelines that ensure AI outputs are reliable, consistent, and improving over time. Regression suites for prompt changes, A/B testing infrastructure for model updates, confidence calibration — because vibes-based testing doesn’t scale at enterprise scale.
MCP tool definitions
LLM-ready tool specs for domain capabilities (Item Store queries, capacity lookups, scenario simulations) that Tempo AI can discover and invoke at runtime within a hub-and-spoke MCP architecture already in production.

Requirements

What you’ll need

A track record of shipping LLM-powered features or products — prototypes don’t count; we want to see things that real users have relied on.
Hands-on experience orchestrating agents — multi-step reasoning, tool use, autonomous action with guardrails. Frameworks like LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent (including rolling your own).
Deep LLM engineering fundamentals: prompt engineering, RAG architectures, function calling / tool use, context management, evaluation-driven development.
Production-quality engineering practices — you write code with tests, participate in code review, care about CI/CD and observability. You build systems that run reliably in production, not notebook prototypes.
Experience with event-driven or streaming data systems — CDC events, real-time pipelines, and the patterns that come with them.
5+ years in software engineering, with 3+ years focused on AI/ML in production systems.
Ability to work embedded in a product team — collaborating daily with domain engineers, product managers, and designers, not just other AI specialists.

Benefits

Comp & perks

Remote First work environment
Unlimited vacation in most of our locations!!
Great benefits including health, dental, vision and savings plan.
Perks such as training reimbursement, WFH reimbursement, and more.
Diverse and dynamic teams with challenging and exciting work.
An opportunity to have a real impact on our business.
A great range of social activities (both in person and virtual).
Optional in person meet-ups and the ability to travel to our international offices
Employee referral program
And so much more!

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

LLM-powered featuresprompt engineeringRAG architecturesfunction callingtool usecontext managementevaluation-driven developmentevent-driven systemsstreaming data systemsproduction-quality engineering

Soft Skills

collaborationmulti-step reasoningautonomous actioncommunication