Staff Ai Engineer

We're building an AI-native platform focused on helping professionals make complex, high-stakes decisions with greater clarity and confidence.

This is not an AI "feature." AI is the product.

As a
Staff AI Engineer
, you will serve as a technical leader responsible for designing, building, and hardening the core intelligence systems behind the platform-systems that directly support real-world decision-making in environments where accuracy and trust are critical.

This is a hands-on role for someone who wants to operate at the edge of what's reliable in applied AI and push those boundaries into production. You will own systems end-to-end: from architecture and modeling decisions through deployment, evaluation, and iteration. You'll also help define technical standards and influence how AI systems are built across the organization.

What You'll Work On

- Architecting
LLM-powered, agentic systems
for research, analysis, and decision support
- Designing
hybrid reasoning pipelines
that combine language models with retrieval systems, structured data, deterministic logic, and external tools
- Building
robust RAG pipelines
over unstructured, noisy, and proprietary datasets
- Developing
evaluation frameworks
to measure reasoning quality, faithfulness, latency, and cost
- Implementing
observability, debugging, and failure handling
for multi-step AI workflows
- Translating ambiguous user needs into reliable, production-grade intelligent behavior in collaboration with product and design
- Raising the bar for
AI engineering practices
through technical leadership and mentorship

Example Problem
Design and build an AI system capable of synthesizing diverse data sources-documents, structured datasets, and external signals-into actionable, well-supported insights, while explicitly surfacing uncertainty and tradeoffs.

Why This Is Challenging

- Product complexity:
The goal is to deliver a system users rely on daily-not a demo or internal prototype
- High-stakes environment:
Outputs must be accurate, explainable, and calibrated-"mostly correct" is insufficient
- Data ambiguity:
Inputs are often incomplete, inconsistent, or contradictory, with no single source of truth
- Reasoning over generation:
The focus is on systems that evaluate, compare, and justify-not just generate fluent responses
- Agent reliability:
Multi-step, tool-using workflows must behave consistently in production environments
- Evaluation is evolving:
You will help define how to measure quality when traditional ML metrics fall short
- Trust as a requirement:
Explainability, traceability, and failure handling are core system properties-not afterthoughts

What We're Looking For

- 6+ years of software engineering experience with significant hands-on work in applied AI/ML systems
- Strong foundation in
Python
and backend system design
- Experience working with
LLMs
, including areas like prompting, fine-tuning, RAG, agentic workflows, or evaluation tooling
- Track record of owning
ambiguous, high-impact systems
from concept through production
- Ability to make thoughtful
architectural tradeoffs
in real-world environments
- Systems-level thinking combined with a bias toward shipping high-quality implementations
- Strong product intuition and a sense of responsibility for end-user outcomes

Bonus Experience

• Background in data-intensive products or regulated environments
• Exposure to domains where correctness, traceability, and trust are critical

Oscar Associates Limited (US) is acting as an Employment Agency in relation to this vacancy.