Research Engineer, Applied AI Engineering
About The Position
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.
Requirements
- A strong engineer who can reason through distributed systems, data pipelines, databases, and are comfortable working across varying programming languages.
- Extremely AI-fluent and actively use modern AI tools to move faster.
- Strong first-principles thinking and can turn ambiguous problems into hypotheses, experiments, and shipped systems.
- Good judgment and taste: you simplify aggressively, avoid unnecessary complexity, and care about maintainability.
- Care about measurement. You do not trust vibes when evals, tests, traces, or data can tell you what is actually happening.
Nice To Haves
- Experience with PyTorch, Hugging Face, vLLM, Ray, MLflow, RAG, fine-tuning & RL, inference serving, and/or model evaluation systems.
Responsibilities
- 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.
Benefits
- Meaningful equity in a venture-backed company defining a new category in commerce intelligence.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
