Research Engineer, Applied AI Engineering

OuterSignal

Backend-focused software engineering role applying ML/AI to user-facing products at OuterSignal showing customer intelligence. Building systems that power AI/data pipeline-related functionalities.

Posted 7/14/2026full-timeNew York City • New York • 🇺🇸 United StatesMid-LevelSenior💰 $200,000 - $300,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and improving AI/data pipelines, integrating with various AI tools, and designing evaluation systems to measure model performance and data quality. Proficient in distributed systems and databases, with a strong focus on maintainability and observability in AI workflows.

Highest-signal resume keywords

AI/Data Pipeline DevelopmentDistributed Systems EngineeringModel Evaluation SystemsJupyter Notebook DesignProgramming Language Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI FluencyData Pipeline EngineeringModel Fine-TuningDistributed SystemsDatabase ManagementExperimentation TechniquesObservability ImplementationHypothesis TestingData Quality MeasurementModel Routing

Soft Skills

First-Principles ThinkingGood JudgmentSimplificationProblem-Solving

Tools & Technologies

TemporalPostgresClickHouseKubernetesPyTorchHugging FaceVLLMRayMLflowRAG

Industry Keywords

AI WorkflowsModel BehaviorExtraction AccuracyEnd-to-End Customer ImpactInference Serving

Tech Stack

Tools & technologies

Distributed SystemsKubernetesPostgresPyTorchRay

About the role

Key responsibilities & impact

Build and improve production AI/data pipelines that run across LLMs, APIs, databases, and workflow systems like Temporal, Postgres, ClickHouse, and Kubernetes.
Design evals and build Jupyter notebooks that help us measure model behavior, data quality, extraction accuracy, and end-to-end customer impact.
Build observability into AI workflows so we can understand cost, latency, reliability, and quality.
Experiment with new models, retrieval strategies, structured-output techniques, prompt/program architectures, and model-routing approaches.
Integrate with fast-changing research and AI APIs, understand their behavior deeply, and build robust abstractions around them.
Help define the foundation for future inference serving, fine-tuning, dataset generation, and model evaluation infrastructure.

Requirements

What you’ll need

A strong engineer who can reason through distributed systems, data pipelines, databases, and are comfortable working across varying programming languages.
You are extremely AI-fluent and actively use modern AI tools to move faster.
You have strong first-principles thinking and can turn ambiguous problems into hypotheses, experiments, and shipped systems.
You have good judgment and taste: you simplify aggressively, avoid unnecessary complexity, and care about maintainability.
You care about measurement. You do not trust vibes when evals, tests, traces, or data can tell you what is actually happening.
**Bonus:** You have experience with PyTorch, Hugging Face, vLLM, Ray, MLflow, RAG, fine-tuning & RL, inference serving, and/or model evaluation systems.

Benefits

Comp & perks

Massive upside: Meaningful equity in a venture-backed company defining a new category in commerce intelligence.
Real AI systems: You’ll work on production AI that directly affects customers, data quality, and revenue — not demos or toy agents.
Big surface area: You’ll help connect research ideas to production systems and customer-facing product.
Right moment: We’re past prototype, real brands already rely on us, and we have strong PMF — but the ceiling is still wide open.