Machine Learning Engineer - Agentic AI

At SignalLabs, we are passionate about enabling enterprises to turn their data into decisions that move the world forward, from helping financial institutions detect risk in real time to accelerating how healthcare and industrial teams act on critical signals. We do this by building and running SignalOS and SignalGraph, the platform behind the world's most demanding signal intelligence workloads, so our customers can convert raw events into context, context into hypothesis and reasoning, and hypothesis into Attention. Founded by engineers and customer obsessed we leap at every opportunity to solve hard technical challenges, from exploring connected data to scaling our infrastructure across billions of signals a day. And we're only getting started. The impact you'll have: As a software engineer with a large language model focus, you will work with your team to build infrastructure and products for the SignalLabs.ai platform at scale. Our backend teams span the architectural pillars that power signal intelligence end-to-end: Data Discovery, where we crawl, profile, and onboard heterogeneous data sources into the platform; the Semantic Module, where raw entities and events are resolved into a unified, meaning-rich representation; the Signal Detection and Correlation Engine, where signals are scored, joined, and elevated into high-confidence signals across time and context; the System of Attention, where the most consequential signals are routed, ranked, and surfaced to the right decision-maker at the right moment; and the Reinforcement Learning Feedback Loop, where every human and machine response is captured to continuously sharpen detection, correlation, and prioritization. You'll own meaningful surface area in one of these domains while shaping how they compose into a single, coherent platform. You will design and implement agentic systems built around large language models LLMs that extend beyond traditional machine learning pipelines. The work will require making tradeoffs between latency, cost, accuracy, andJob Description: ML Engineer1 controlability, including decisions between deterministic pipelines and adaptive, LLM-driven approaches within agentic system design. Responsibilities Build systems that combine models, tools, and data into cohesive, agentic workflows capable of executing multi-step tasks. This includes designing system behaviors such as planning, tool use, structured outputs, and failure handling. Develop infrastructure for evaluating and improving agentic system performance, including quality, reliability, and cost, and build monitoring and observability systems to understand behavior in production. Integrate LLMs with internal and external tools, enabling agentic systems to retrieve context, call APIs, and execute actions as part of end-to-end workflows. Collaborate with cross-functional teams to translate product requirements into scalable agentic systems, and continuously improve system performance through iteration and evaluation. Minimum Qualifications Proven knowledge of cutting-edge agentic systems. Demonstrated experience designing and shipping agentic systems in production environments. Strong proficiency with LLM-assisted coding, including using AI tools to design, implement, and iterate on complex systems. Proven ability to design end-to-end systems, making architectural decisions across multiple components (e.g., services, data pipelines, integrations). Preferred Qualifications Demonstrated track record of building and shipping agentic or LLM-based products, with visible portfolio (e.g., apps, open-source projects), orJob Description: ML Engineer2 recognized contributions such as publications in top-tier conferences or impactful technical work. Strong system design experience, including defining and evolving architecture for complex, multi-component systems. Experience taking products from concept to launch, including delivering user- facing applications at scale or in real-world environments. What we look for: * BS (or higher) in Computer Science, or a related field * 5+ years of production level experience in one of: Python, Java or similar language * Experience with developing and deploying Large Language Models and developing Small Language Models * Experience developing large-scale distributed systems * Experience with cloud technologies, e.g. AWS, Azure, GCP, or KubernetesJob Description: ML Engineer3