Machine Learning Infrastructure Engineer, GenAI Technology
AI in this role
A Career with Point72's Technology Team
As Point72 reimagines the future of investing, our Technology team is constantly evolving our firm’s IT infrastructure and engineering capabilities, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision making and enhance how we build and operate our platforms and applications.
As a member of Point72’s Technology team, we encourage and support your professional development from day one—helping you advance your technical skills, contribute innovative ideas, and satisfy your own intellectual curiosity—all while delivering real business impact for our multi-billion-dollar global business.
WHAT YOU'LL DO
- Design and implement high-performance infrastructure to support large-scale generative AI and machine learning workloads, enabling faster model iteration and real business impact
- Design and operate distributed systems for model training, hyperparameter tuning, inference, and data preprocessing pipelines to deliver reliable end-to-end machine learning (ML) workflows
- Collaborate with ML researchers and engineers to produce models, optimizing compute utilization, training throughput, and inference latency
- Develop and automate deployment, orchestration, and CI/CD pipelines for models and data workflows using container orchestration and infrastructure-as-code (IaC)
- Implement observability, monitoring, and cost-management strategies for GPU and accelerator compute environments to maintain predictable performance and spend
- Evaluate, integrate, and benchmark emerging hardware and software technologies across cloud and on-prem environments to improve scalability and throughput
- Drive security, compliance, and operational runbooks for GenAI infrastructure including access controls, secrets management, and incident response procedures
- Troubleshoot, profile, and optimize performance across GPU and CPU compute stacks to remove bottlenecks and increase reliability
- Document architecture, operational practices, and mentor engineers to expand team capability and accelerate adoption of production-ready GenAI infrastructure
WHAT'S REQUIRED
- Bachelor's or master's degree in computer science, electrical engineering, or a related technical field
- 3–7 years of experience building and maintaining scalable compute or machine learning infrastructure systems
- Deep understanding of distributed systems, container orchestration (Kubernetes), and public cloud platforms such as AWS, Google Cloud Platform, or Azure
- Hands-on experience with machine learning operations and infrastructure tools such as MLflow, Ray, Airflow, Kubeflow, and Terraform
- Strong understanding of reinforcement learning concepts and their infrastructure implications
- Proficiency in Python and systems-level programming in one or more languages such as Go, C++, or Rust
- Strong debugging, performance profiling, and optimization skills across GPU and CPU compute stacks
- Experience implementing monitoring, observability, and cost-optimization for GPU/accelerator-based compute environments
- Excellent collaboration and communication skills with a systems-thinking mindset
- Commitment to the highest ethical standards
WE TAKE CARE OF OUR PEOPLE
We invest in our people, their careers, their health, and their well-being. When you work here, we provide:
- Fully-paid health care benefits
- Generous parental and family leave policies
- Volunteer opportunities
- Support for employee-led affinity groups representing women, people of color and the LGBT+ community
- Mental and physical wellness programs
- Tuition assistance
- A 401(k) savings program with an employer match and more
ABOUT POINT72
Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry's brightest talent by cultivating an investor-led culture and committing to our people's long-term growth. For more information, visit https://point72.com/.
The annual base salary range for this role is $180,000-$300,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
How we rate this
Machine Learning Infrastructure Engineer, GenAI Technology at Point72 rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used Mlflow hands-on?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Mlflow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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