Job description

Position Overview We are seeking a  Vice President – Technical Program Manager (TPM)  to drive the vision, program execution, and cross-functional delivery of our central  Autonomous Alpha Generation Platform . Positioned at the convergence of quantitative research, artificial intelligence, automated trading systems and scalable platform engineering, this ecosystem enables autonomous AI/ML agents and quaVntitative researchers to explore signal spaces, engineer features, evaluate statistical models, and run high-fidelity simulations. In this role, the TPM serves as a key strategic facilitator, program leader, and platform enabler. The primary focus is defining, building, and deploying a modular, extensible central infrastructure that seamlessly integrates into the existing workflows, software architectures, and specialized strategies of individual trading desks. The ideal candidate combines strong technical platform leadership with experience in machine learning and automated trading systems with structured project and program management discipline. You will lead cross-functional delivery across engineering, research, and business teams, architecting pluggable framework interfaces that allow internal teams to customize capabilities while embedding human control points and intuitive user interfaces into agentic workflows. Key Responsibilities 1. Platform Leadership & Program Management • Drive end-to-end project management and program execution for the central platform, establishing clear delivery milestones, release schedules, and cross-functional dependencies across multi-team engineering sprints. • Lead integration initiatives to tie central platform infrastructure into the desk-specific applications, execution systems, and strategy frameworks of individual trading teams. • Define and measure platform adoption, developer experience (DX), throughput velocity, and system reliability metrics across internal client groups. • Coordinate cross-functional alignment across Quantitative Research, Core Technology, Data Engineering, Infrastructure, and Risk Governance teams. 2. Extensible Frameworks & Modular Integration • Drive an open, highly flexible platform architecture, establishing SDKs, APIs, and plugin standards that enable internal clients and trading desks to easily plug in their own proprietary capabilities, custom analytical modules and execution simulators. • Abstract core platform mechanisms so desk quants retain full flexibility over their proprietary trading logic while benefiting from centralized compute, data storage, and automated strategy exploration infrastructure. • Maintain standardized validation contracts, parameter sweep frameworks, and cross-asset evaluation hooks across all integrated plugins. 3. Human Control Points & User Interface Design • Define requirements for Human-in-the-Loop (HITL) control mechanisms, establishing structured approval gates, confidence thresholds, and real-time intervention capabilities within autonomous agent workflows. • Address user interface (UI/UX) challenges by partnering with front-end engineering teams to deliver intuitive dashboards and control centers that provide quants and portfolio managers with real-time visibility, inspection tools, audit trails, and manual override controls over agent decisions. • Ensure all workflow checkpoints comply with internal Model Risk Management (MRM) and regulatory oversight standards. 4. Core Data & MLOps Infrastructure • Direct requirements for scalable, central data and feature infrastructure, ensuring strict point-in-time integrity and dataset lineage tracking across raw, synthetic, and model-derived data assets. • Establish unified cataloging, metadata searchability, and data discovery interfaces to support seamless asset exploration by both human researchers and automated agents. Required Qualifications & Experience • Experience:  7+ years of experience in technical program management, platform engineering management, or enterprise software delivery within financial technology, investment banking, quantitative hedge funds, or enterprise SaaS. • Program & Project Management:  Demonstrated success leading complex, multi-team infrastructure projects, managing technical roadmaps, tracking dependencies, and successfully integrating central platform capabilities into client-facing or desk-specific environments. • Platform Architecture & Extensibility:  Proven ability to define platform requirements for modular architectures, open plugin frameworks, APIs, and SDKs designed for internal developer ecosystems. • Human-in-the-Loop & UI/UX Design:  Hands-on experience defining workflow control points, governance checkpoints, and user-facing monitoring/dashboard interfaces for automated or semi-autonomous software systems. • Machine Learning & MLOps Infrastructure: • Demonstrated experience with enterprise MLOps architecture, feature stores, data cataloging, model registries, and automated lineage tracking. • Experience managing time-series data infrastructure, including high-frequency tick data storage, time-series transformations, and point-in-time compliance. • Familiarity with autonomous agent design patterns (e.g., tool usage, planning loops, intervention thresholds, dynamic execution). • Domain Awareness:  Familiarity with quantitative trading concepts, financial markets, or systematic research workflows. Desirable Technical Capabilities • Programming & Frameworks:  Proficiency with Python (e.g., Pandas, PyTorch, Ray, Celery) and C++ integration patterns for high-performance enterprise platforms. • Front-End & UI Tooling:  Familiarity with modern user interface frameworks, operational monitoring tools, and workflow visualization applications. • Compute & Data Infrastructure:  Understanding of modern distributed compute orchestration (e.g., Ray, Kubernetes), time-series databases, and cloud data lake architectures (e.g., Apache Parquet, Iceberg). Salary Range The expected base salary for this New York, New York, United States-based position is $150,000-$300,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end. Benefits Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non-temporary, full-time and part-time US employees who work at least 20 hours per week, can be found here . ABOUT GOLDMAN SACHS At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: goldmansachs.com © The Goldman Sachs Group, Inc., 2026. All rights reserved.

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Position Overview
Platform Leadership & Program Management
Drive end-to-end project management and program execution for the central platform, establishing clear delivery milestones, release schedules, and cross-functional dependencies across multi-team engineering sprints.
Lead integration initiatives to tie central platform infrastructure into the desk-specific applications, execution systems, and strategy frameworks of individual trading teams.
Define and measure platform adoption, developer experience (DX), throughput velocity, and system reliability metrics across internal client groups.
Coordinate cross-functional alignment across Quantitative Research, Core Technology, Data Engineering, Infrastructure, and Risk Governance teams.
Extensible Frameworks & Modular Integration
Abstract core platform mechanisms so desk quants retain full flexibility over their proprietary trading logic while benefiting from centralized compute, data storage, and automated strategy exploration infrastructure.
Maintain standardized validation contracts, parameter sweep frameworks, and cross-asset evaluation hooks across all integrated plugins.
Human Control Points & User Interface Design
Define requirements for Human-in-the-Loop (HITL) control mechanisms, establishing structured approval gates, confidence thresholds, and real-time intervention capabilities within autonomous agent workflows.
Ensure all workflow checkpoints comply with internal Model Risk Management (MRM) and regulatory oversight standards.
Core Data & MLOps Infrastructure
Direct requirements for scalable, central data and feature infrastructure, ensuring strict point-in-time integrity and dataset lineage tracking across raw, synthetic, and model-derived data assets.
Establish unified cataloging, metadata searchability, and data discovery interfaces to support seamless asset exploration by both human researchers and automated agents.
Required Qualifications & Experience
Platform Architecture & Extensibility:  Proven ability to define platform requirements for modular architectures, open plugin frameworks, APIs, and SDKs designed for internal developer ecosystems.
Human-in-the-Loop & UI/UX Design:  Hands-on experience defining workflow control points, governance checkpoints, and user-facing monitoring/dashboard interfaces for automated or semi-autonomous software systems.
Demonstrated experience with enterprise MLOps architecture, feature stores, data cataloging, model registries, and automated lineage tracking.
Experience managing time-series data infrastructure, including high-frequency tick data storage, time-series transformations, and point-in-time compliance.
Familiarity with autonomous agent design patterns (e.g., tool usage, planning loops, intervention thresholds, dynamic execution).
Domain Awareness:  Familiarity with quantitative trading concepts, financial markets, or systematic research workflows.
Desirable Technical Capabilities

Nice-to-haves

The expected base salary for this New York, New York, United States-based position is $150,000-$300,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.

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Keywords to mirror

aimlmachine learningpythonkubernetes

Platform: goldman-sachs
Effort: medium

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Vision

Wellness