CS
Founding Forward Deployed Engineer
Accepting applicationsCognatio Solutions · United States
Full-Time Mid_senior AIJavaPythonaiate
Posted
28 May
Category
Manufacturing
Experience
Mid_senior
Country
United States
Forward Deployed Engineer
Remote (US)
About the company
A well-backed startup building observability tooling for teams running software in production. The product collects detailed runtime data from live systems, giving engineers (and increasingly the AI tools they code with) a clear picture of how code actually behaves under real traffic and real failures. The SDK is lightweight, fast to install, and adds little overhead. They already work with engineering teams running serious production workloads.
Small team, quick shipping cadence, a lot of individual ownership.
The role
This sits between software engineering, production debugging, and hands-on customer work. You go into customer production environments, get the SDK deployed, dig into real issues, and prove out the product by actually fixing things. What you learn in the field feeds back into the product as reusable tooling and, over time, roadmap input.
The harder part of the job is operating inside live applications: understanding runtime behaviour under load and designing changes safe enough for teams to trust in their most critical services.
What you'll do
Own technical delivery end to end, across both customer engagements and internal tooling
Deploy and integrate the SDK into real-world production systems spanning different stacks and environments
Debug genuine production problems (latency, memory, reliability, awkward edge cases) and resolve them
Write and ship production-quality code, mostly field tooling and internal systems
Build automation that turns production signals into actions earlier in the development cycle
Turn recurring customer problems into reusable assets (runbooks, reference setups, demo environments) and product improvements
Run technical customer sessions: calls, live debugging, written follow-up
Work with Product and Engineering to convert field observations into features
What you need
6+ years in software engineering, solutions or field engineering, or a technical lead role
Strong backend fundamentals and a real track record debugging production systems
Solid in at least one of Node.js/TypeScript, Python, or Java (JVM)
Hands-on experience with latency, memory issues, regressions, and distributed system failures in production
Comfortable with modern backend architecture: microservices, event-driven systems, containers and orchestration (Docker, Kubernetes)
Experience building or integrating SDKs, developer tools, or other production-facing components
Performance work: CPU and memory profiling, keeping overhead low
Happy working at pace somewhere early-stage where you own a lot
Nice to have
Background in observability, performance monitoring, or developer tooling
Familiarity with AI-assisted development: LLM agents, evals, guardrails, human-in-the-loop
Knowledge of runtime internals (event loops, garbage collection, JIT, tracing hooks)
Experience with APM agents, tracing, or telemetry pipelines
Low-overhead instrumentation techniques: sampling, bytecode manipulation, eBPF
Show more Show less
Remote (US)
About the company
A well-backed startup building observability tooling for teams running software in production. The product collects detailed runtime data from live systems, giving engineers (and increasingly the AI tools they code with) a clear picture of how code actually behaves under real traffic and real failures. The SDK is lightweight, fast to install, and adds little overhead. They already work with engineering teams running serious production workloads.
Small team, quick shipping cadence, a lot of individual ownership.
The role
This sits between software engineering, production debugging, and hands-on customer work. You go into customer production environments, get the SDK deployed, dig into real issues, and prove out the product by actually fixing things. What you learn in the field feeds back into the product as reusable tooling and, over time, roadmap input.
The harder part of the job is operating inside live applications: understanding runtime behaviour under load and designing changes safe enough for teams to trust in their most critical services.
What you'll do
Own technical delivery end to end, across both customer engagements and internal tooling
Deploy and integrate the SDK into real-world production systems spanning different stacks and environments
Debug genuine production problems (latency, memory, reliability, awkward edge cases) and resolve them
Write and ship production-quality code, mostly field tooling and internal systems
Build automation that turns production signals into actions earlier in the development cycle
Turn recurring customer problems into reusable assets (runbooks, reference setups, demo environments) and product improvements
Run technical customer sessions: calls, live debugging, written follow-up
Work with Product and Engineering to convert field observations into features
What you need
6+ years in software engineering, solutions or field engineering, or a technical lead role
Strong backend fundamentals and a real track record debugging production systems
Solid in at least one of Node.js/TypeScript, Python, or Java (JVM)
Hands-on experience with latency, memory issues, regressions, and distributed system failures in production
Comfortable with modern backend architecture: microservices, event-driven systems, containers and orchestration (Docker, Kubernetes)
Experience building or integrating SDKs, developer tools, or other production-facing components
Performance work: CPU and memory profiling, keeping overhead low
Happy working at pace somewhere early-stage where you own a lot
Nice to have
Background in observability, performance monitoring, or developer tooling
Familiarity with AI-assisted development: LLM agents, evals, guardrails, human-in-the-loop
Knowledge of runtime internals (event loops, garbage collection, JIT, tracing hooks)
Experience with APM agents, tracing, or telemetry pipelines
Low-overhead instrumentation techniques: sampling, bytecode manipulation, eBPF
Show more Show less