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
