Research Engineer, Applied AI Engineering

Outersignal
New York, NY
On-site

Who this role is best for

Geared toward mid-level engineers comfortable with AI/data pipelines and distributed systems in a New York City-based startup environment.

Best fit for

  • Engineers who simplify complex AI workflows into maintainable systems.
    — “you simplify aggressively, avoid unnecessary complexity”
  • Candidates fluent in modern AI tools and distributed systems.
    — “extremely AI-fluent and actively use modern AI tools”
  • Backend-focused developers with hands-on experience in production AI pipelines.
    — “backend-focused software engineering role specializing in applying ML/AI”

Things to consider

  • Role requires deep integration with fast-changing AI research APIs.
    — “Integrate with fast-changing research and AI APIs”
  • Expect to build systems for inference serving and fine-tuning.
    — “future inference serving, fine-tuning, dataset generation”

How to stand out

  • Demonstrate concrete examples of measuring AI system performance.
    — “care about measurement. You do not trust vibes”
  • Highlight experience with Temporal, Kubernetes, or ClickHouse.
    — “workflow systems like Temporal, Postgres, ClickHouse”
  • Showcase projects where you turned ambiguous problems into shipped systems.
    — “turn ambiguous problems into hypotheses, experiments”
Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · IndividualLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build and improve production AI/data pipelines
  • design evals
  • build observability
  • integrate with fast-changing research and AI APIs
Typical background
backend developmentAIML

Skills & requirements

Required

Backend DevelopmentAIMLData PipelinesDistributed Systems

Preferred

PyTorchHugging FaceVllmRayMlflowRAGFine-tuningInference ServingModel Evaluation

Stack & domain

LlmsApisDatabasesTemporalPostgreSQLClickhouseKubernetesJupyter NotebooksPyTorchHugging FaceVllmRayMlflowRagFine-tuningRlInference ServingModel Evaluation SystemsFirst-principles ThinkingJudgmentTasteSimplificationMeasurementAIMLData PipelinesObservabilityModel BehaviorData QualityExtraction AccuracyCustomer ImpactModel-routingPrompt/program ArchitecturesCostLatencyReliabilityQuality

About the role

Original posting from Outersignal via Ashby

About OuterSignal

We are building the customer intelligence layer for commerce. Brands know when an order comes in, but they only see ~5% of the real story. We show them the other 95%: not just who bought, but who that person is — the execs, influencers, journalists, retail buyers, investors, and everyday customers who become a brand’s best evangelists. Our platform enriches every order in real time with professional and personal signals, builds personas, and powers everything from surprise-and-delight outreach to smarter email flows, analytics, and BD leads. We’re a small, high-performing team building the category-defining platform for e-commerce customer intelligence, backed by a world-class investor base.

ABOUT THIS ROLE:

This is a backend-focused software engineering role specializing in applying ML/AI to user-facing products. You will work on the systems that power OuterSignal’s AI/data pipeline: prompt chaining, eval design, research APIs, model routing, and prod orchestration. The evolution of this role will include inference serving, fine-tuning, and synthetic dataset generation.

WHAT YOU’LL DO:

  • 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.

WHO YOU ARE:

  • 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.

WHY JOIN NOW:

  • Early seat: You’ll help shape the DNA of OuterSignal’s Applied AI Engineering org from the beginning.
  • 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.

Source: Outersignal careers (Ashby)

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