Research Engineer, Applied AI Engineering

US$ 200 000 – 300 000 al año · Tiempo completo · Híbrido

  • New York City · NY
  • Publicada hace 121 días
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Descripción del empleo

About OuterSignal

We are building the customer intelligence and personalization layer for consumer businesses. Every business knows when an order comes in or a user signs up, but they only see 5% of the real story.

We show them the other 95%: not just who’s buying, but who that person actually is. That includes the execs, influencers, journalists, retail buyers, investors, and everyday customers who become a brand’s best evangelists. Our platform enriches every customer 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 customer intelligence, backed by a world-class investor base.

 

The Opportunity

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.

This is production AI that directly affects customers, data quality, and revenue, not demos or toy agents. You’ll help connect research ideas to production systems and customer-facing product. We’re past prototype with strong product-market fit, and the ceiling is wide open.

 

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

 

What We’re Looking For

  • A strong engineer who can reason through distributed systems, data pipelines, and databases, and is comfortable working across programming languages

  • Extremely AI-fluent, actively using modern AI tools to move faster

  • Strong first-principles thinking: you turn ambiguous problems into hypotheses, experiments, and shipped systems

  • Good judgment and taste: you simplify aggressively, avoid unnecessary complexity, and care about maintainability

  • A focus on measurement: you don’t trust vibes when evals, tests, traces, or data can tell you what is actually happening

  • Bonus: experience with PyTorch, Hugging Face, vLLM, Ray, MLflow, RAG, fine-tuning and RL, inference serving, or model evaluation systems

 

What We Offer

  • Impact: Join at the ground floor where your work directly influences company trajectory

  • Growth: Real brands already rely on us and we’re scaling fast, which means exceptional learning opportunities and room to grow with the company

  • Upside: Meaningful equity in a venture-backed company defining a new category

  • Team: Work directly with the founders and experienced operators

  • Ownership: An early seat shaping OuterSignal’s Applied AI Engineering org from the beginning

 

How to Apply

Submit your resume along with responses to the application questions. We review applications on a rolling basis and will reach out to qualified candidates to discuss potential opportunities.

OuterSignal is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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