СШАForward Deployed Engineer (FDE), Financial Services- NYC
3 нед. назад
185k–300k USD / годСШАSeniorHybrid
ai systemsmodel deploymentworkflow automationrisk management
Lead end-to-end deployments of AI models inside financial services organizations ensuring correctness, latency, explainability, and control.
О компании
- ’s 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. We operate at the intersection of customer delivery and core platform development, embedding deeply with customers to translate frontier model capabilities into reliable, auditable systems that create measurable business impact. Our work turns early deployments into repeatable solution patterns, operating standards, and evaluation practices that scale across regulated financial institutions.
- is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
- Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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- At , we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Обязанности
- We are hiring a Forward Deployed Engineer (FDE) to lead end-to-end deployments of ’s models inside financial services organizations where correctness, latency, explainability, and control matter. You will work with customers who are experts in investment banking, trading, risk, compliance, underwriting, research, operations, or investment decision-making, translating complex workflows, data constraints, and regulatory requirements into production systems.
- You will measure success through production adoption, workflow efficiency, risk reduction, revenue impact, and evaluation-driven feedback loops that inform product, model, and GTM strategy. You’ll work closely with Product, Research, GTM, Security, Legal, and GRC to deliver systems that meet enterprise standards for governance, auditability, and operational resilience.
- You will also play a central role in shaping ’s Financial Services offering — identifying high-value use cases, defining solution patterns, and building the first repeatable deployments that scale across institutions. Learn more about some of our work with financial institutions .
- This role is based in New York. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% may be required.
- Design and ship production AI systems around models, owning integrations, data flows, reliability, observability, and on-call readiness across financial workflows.
- Lead discovery and scoping from pre-sales through post-production, translating ambiguous business problems into hypothesis-driven problem framing, system requirements, and delivery plans with measurable outcomes.
- Define and enforce launch criteria for regulated financial environments, including controls, audit artifacts, evaluation benchmarks, and acceptance thresholds tied to risk and performance.
- Build in sensitive data environments where access controls, data lineage, explainability, and failure modes shape architecture and operating procedures.
- Run evaluation loops that measure model and system quality against workflow-specific financial benchmarks (e.g., accuracy, latency, coverage, false positives) and use results to drive iteration.
- Own delivery state across multiple workstreams, making trade-offs between scope, speed, and quality to protect production outcomes.
- Distill deployment learnings into hardened primitives, reference architectures, playbooks, and tooling that scale across financial institutions.
- Surface field feedback that informs model behavior, product capabilities, and platform gaps in real-world financial use cases.
- Bring 5+ years of software engineering, ML engineering, or technical deployment experience with customer-facing ownership in financial services or adjacent regulated industries.
- Have owned complex AI or data-driven systems end-to-end, from scoping through production adoption, in environments where errors carry real financial or regulatory consequences.
- Have experience with financial workflows such as research, trading, risk, compliance, underwriting, operations, or investment processes, and understand how incentives and controls shape adoption.
- Write and review production-grade code across backend and frontend systems using Python, JavaScript, or comparable stacks.
- Have deployed systems powered by LLMs or generative models and understand how model behavior, evaluation, and guardrails affect user trust and business outcomes.
- Communicate clearly across engineering, product, risk, compliance, and executive stakeholders, translating technical trade-offs into decision quality and risk posture.
- Apply systems thinking with high execution standards, turning incidents, escalations, and failures into improved operating standards and repeatable deployment patterns.
- Stay calm and decisive when delivery is urgent, ambiguous, and high-stakes.