Staff Research Engineer – RRI Harness

Sequen

. Build and evolve the Python multi-agent runtime, including orchestration, tool contracts, context and memory management, compaction, and model adapters across LLM providers .

Posted 10/9/2026full-timeRemote • United StatesLead💰 $300,000 - $350,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in building and evolving Python-based multi-agent systems, with a strong focus on LLM integration, distributed systems, and resilience engineering. Proficient in debugging across various environments and implementing evaluation infrastructures for machine learning experiments.

Highest-signal resume keywords

Python ProgrammingDistributed Systems ExperienceLLM IntegrationKubernetes ProficiencyApplied Research in ML

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonDistributed SystemsLLMsDebuggingKubernetesPyTorchExperiment TrackingEvaluation InfrastructureGoML CI

Tools & Technologies

GPU NodesOn-Prem EnvironmentsBYOCAir-Gapped Environments

Industry Keywords

Multi-Agent RuntimeContext ManagementMemory ManagementTool ContractsModel AdaptersEvent StreamsToken AccountingComponent AblationsProduction SoftwareResilience Engineering

Tech Stack

Tools & technologies

KubernetesNode.jsPythonPyTorchGo

About the role

Key responsibilities & impact

Build and evolve the Python multi-agent runtime, including orchestration, tool contracts, context and memory management, compaction, and model adapters across LLM providers
Design and run experiments on agent prompting, tool design, context and memory strategies, and model choice
Ship changes that measurably improve RRI results
Deliver a fixed evaluation set, scorecard with measured noise bands, per-change harness evaluations, and component ablations
Engineer resilience across GPU failures, node replacement, rolling upgrades, provider errors, and credit limits
Build event streams, cross-session analysis, per-agent cost and token accounting, and MCP administration tooling
Identify and reduce agent idling, looping, and excessive token usage
Design interfaces with the Go control plane and GPU proxy owners
Partner with applied scientists and forward-deployed engineers to diagnose and resolve production client issues
Own the core runtime of Sequen's autonomous research engine

Requirements

What you’ll need

7+ years of experience building production software
Strong Python skills
History of distributed or long-running systems
Experience building with LLMs in agentic loops, including tool calling, prompt and context management, streaming, and retries
Ability to debug across process, container, and network boundaries
Experience with Kubernetes and GPU nodes
Ability to read a training script, understand a ranking metric, and distinguish real regressions from noise
Applied research experience with ML or LLM experiments, baselines, and ablations
Experience with evaluation infrastructure, test harnesses, benchmarks, experiment tracking, or ML CI
Working knowledge of Go
Experience with on-prem, BYOC, or air-gapped environments and security constraints
Experience with PyTorch training at scale, GPU scheduling, or ML platforms
Contributions to agent frameworks, eval harnesses, or ML tooling are advantageous

Benefits

Comp & perks

Unlimited paid time off
Flexible hybrid/remote configurations
Highly collaborative, world-class engineering culture
Equity