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.
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.
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.
Skills Required
- Build and improve production AI/data pipelines across LLMs, APIs, databases, and workflow systems
- Experience with workflow and orchestration systems such as Temporal and Kubernetes
- Experience with databases like Postgres and ClickHouse
- Design evals and build Jupyter notebooks for measuring model behavior and data quality
- Build observability into AI workflows to measure cost, latency, reliability, and quality
- Experiment with models, retrieval strategies, structured-output techniques, prompt/program architectures, and model-routing
- Integrate with research and AI APIs and build robust abstractions around them
- Strong engineering ability across distributed systems, data pipelines, and databases
- AI fluency and active use of modern AI tools
- First-principles thinking; turn ambiguity into hypotheses, experiments, and shipped systems
- Measurement-driven mindset; use evals, tests, traces, and data to validate systems
- Experience with PyTorch
- Experience with Hugging Face
- Experience with vLLM
- Experience with Ray
- Experience with MLflow
- Experience with RAG (retrieval-augmented generation)
- Experience with fine-tuning and RL
- Experience with inference serving and model evaluation systems
What We Do
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 + personal signals, unlocks personas, and powers everything from surprise-and-delight outreach to smarter email flows, analytics, and BD leads. We serve 150+ brands and are growing 50% month-over-month - all within our first 6 months of operation. We're a small, high-performing team building the category-defining platform for e-commerce customer intelligence, backed by a world-class investor base.
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