Forward Deployed Engineer (FDE), Financial Services- NYC
About this role
The Forward Deployed Engineering team partners with banks, asset managers, insurers, and private capital firms to deploy production-grade AI systems in high-stakes financial environments. Practitioners in this function bridge customer delivery and core platform development by embedding directly with institutions to turn early deployments into repeatable solution patterns, operating standards, and evaluation practices.
As a Forward Deployed Engineer focusing on financial services, you will lead end-to-end deployments where correctness, latency, explainability, and control are critical. You will collaborate with specialists across investment banking, trading, risk, compliance, underwriting, and research to translate complex workflows and regulatory requirements into reliable systems. Success in this position involves driving production adoption, measuring workflow efficiency, and establishing evaluation-driven feedback loops that inform broader product and go-to-market strategies.
Ideal candidates bring substantial technical deployment experience in regulated industries alongside strong systems thinking and communication skills. You will thrive here if you enjoy navigating ambiguous, high-stakes environments and turning field insights into hardened reference architectures that scale across financial institutions.
What you will do
- Design and ship production artificial intelligence systems around models by owning integrations, data flows, reliability, observability, and on-call readiness across financial workflows.
- Lead discovery and scoping from pre-sales through post-production to translate ambiguous business problems into structured system requirements and delivery plans.
- Define and enforce launch criteria for regulated financial environments, including controls, audit artifacts, and evaluation benchmarks.
- Build applications within sensitive data environments where access controls, data lineage, and explainability shape the underlying architecture.
- Run evaluation loops that measure model and system quality against workflow-specific financial metrics such as accuracy and latency.
- Own delivery state across multiple workstreams by balancing scope, speed, and quality to protect production outcomes.
- Distill deployment learnings into hardened primitives, reference architectures, and playbooks that scale across institutions.
What you need
- Bring five or more years of software engineering, machine learning engineering, or technical deployment experience with customer-facing ownership.
- Have owned complex artificial intelligence or data-driven systems end-to-end in environments where errors carry real financial or regulatory consequences.
- Possess familiarity with financial workflows such as research, trading, risk, compliance, underwriting, or investment processes.
- Write and review production-grade code across backend and frontend systems using Python, JavaScript, or comparable stacks.
- Have deployed systems powered by large language models or generative models while understanding how model behavior and guardrails affect trust.
- Communicate clearly across engineering, product, risk, compliance, and executive stakeholders to translate technical trade-offs into decision quality.
Benefits
- Offers Equity
- Relocation assistance
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
This role is based in New York and utilizes a hybrid work model requiring three days in the office per week. Relocation assistance is available, and travel up to 50% may be required.
What is the salary range for this position?
The compensation for this role ranges from $185K to $300K alongside offered equity.
What kind of team will I be working with?
You will partner with banks, asset managers, insurers, and private capital firms while working closely with Product, Research, GTM, Security, Legal, and GRC teams.
What will I actually work on day-to-day?
You will lead end-to-end deployments of models inside financial organizations, design production systems, manage integrations, and define solution patterns for high-stakes financial workflows.