Artificial Intelligence & Machine Learning Engineer, Vice President - AI Labs

Blackrock

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
New York, NY
Salary
$190,000–$240,000 / yr
Posted
22 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $212k
This role $215k
$169k most similar roles pay here $265k

This role pays more than 54% of similar roles. Most pay $177,900–$246,150 — the shaded band above. At the midpoint, this role pays about $215k versus about $212k for comparable roles.

Based on 240 similar postings.

Employer

About Blackrock

BlackRock is the world''s largest asset management firm, providing investment management, risk management, and advisory services to institutional and retail clients through its Aladdin technology platform. Industry: Asset Management & Financial Services

Blackrock currently has 81 open roles on FindRole.

Listed pay typically runs $147,500–$195,000 across 77 roles with salary data.

Most-posted roles

View all roles at Blackrock

At a glance

TL;DR · Artificial Intelligence & Machine Learning Engineer, Vice President - AI Labs

Role Posting Log in to save

As an Artificial Intelligence & Machine Learning Engineer, Vice President - AI Labs, you will join a hybrid team of scientists and engineers to solve complex challenges in financial services. You will provide technical leadership, set architectural directions, and drive design decisions for production-grade systems including generative AI, intelligent agents, predictive modeling, and optimization engines. Your daily work involves building reusable AI platforms, infrastructure, and data pipelines while establishing engineering standards and mentoring others. The role requires proficiency in Python and SQL, along with experience in Docker, Kubernetes, and frameworks like PyTorch, TensorFlow, or JAX. You will translate emerging capabilities into scalable solutions for investment insights and risk management. Key technical focus areas include optimizing for latency and cost, managing non-deterministic applications, and ensuring security within regulated environments using distributed systems and cloud infrastructure.

Skills

Python SQL Docker Kubernetes PyTorch TensorFlow JAX Generative AI Large Language Models Distributed Systems Data Pipelines API Design Rust C++ Go Zig Machine Learning Optimization Cloud Infrastructure

What you'll do

  • Provide technical leadership and set architectural direction for multidisciplinary teams.
  • Design, build, and operate production AI/ML systems including generative AI and intelligent agents.
  • Develop reusable AI platforms, services, and infrastructure to accelerate firm-wide adoption.
  • Translate emerging AI capabilities into robust, scalable solutions with measurable business impact.
  • Define evaluation, testing, and observability approaches for non-deterministic and agentic applications.
  • Optimize AI systems for latency, throughput, compute efficiency, reliability, security, and cost.
  • Establish engineering standards and mentor engineers to influence technical strategy across teams.

What we're looking for

  • 8+ years of software engineering experience designing and operating complex production systems.
  • 4+ years of experience building and deploying AI/ML, optimization, or model-driven applications in production.
  • Strong proficiency in Python for developing maintainable, well-tested production software.
  • Strong SQL skills with experience in data-intensive applications and large-scale datasets.
  • Experience designing and building APIs, services, data pipelines, or distributed systems for AI/ML applications.
  • Experience with containerization and orchestration technologies such as Docker and Kubernetes.
  • Demonstrated ability to lead technical solutions from architecture through production deployment and operation.
  • Strong problem-solving, collaboration, and communication skills to influence decisions across multidisciplinary teams.

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