Supporting one of the world's largest AI compute environments, operating and scaling multi-cloud infrastructure for GPU-intensive model training and deep learning research.
Compensation
The pay range for this position is $100.00 - $100.00/hr.
Responsibilities
Provision and operate large-scale compute environments
Manage Kubernetes-based infrastructure
Optimize GPU resource allocation
Support Slurm scheduling systems
Build automation that enables rapid AI platform growth
Research and develop systematic strategies across futures, with a strong emphasis on commodities, applying statistical and machine learning.
Own the research process end to end: experiment design, dataset generation, time-series analysis, feature engineering, model building, backtesting, and productionizing the signal.
Run research workloads efficiently on our distributed, cloud-based compute platform, and work with the platform engineer to shape new capabilities.
Collaborate closely with traders, engineers, and core technology teams to find the best expression of new ideas in the portfolio.
Document research findings with academic rigor
Requirements
BS
MS
PhD in Statistics, Mathematics, Physics, Computer Science, Engineering, Quantitative Finance, or a related quantitative field
Nice to have: successful quantitative trading strategies
As a Senior Machine Learning Engineer at Iterable, you will design and build end-to-end machine learning systems that power intelligence across our product.
Responsibilities
Build and ship core product ML capabilities including send-time/frequency optimization, recommendation engines, and campaign personalization.
Own the full model lifecycle end-to-end: EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation (e.g., multi-armed bandits, ensemble models).
Build and scale our data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes.
Partner with Backend and Platform teams to elevate feature serving, improve overall data architecture, and establish engineering standards across the team.
Mentor engineers on the team and help set the technical direction for ML at Iterable.
Design and operate large-scale data pipelines for Alexa AI personalization and proactive experiences.
Responsibilities
Build and operate ETL/ELT pipelines processing billions of records a day
Partner with SDEs and applied scientists to define data strategy, contracts, and feature pipelines
Own operational health: data migrations, schema/platform updates, and triage of pipeline tickets
Define data contracts, feature pipelines, and data-quality checks feeding personalization and proactive-agent models
Operate and continuously improve production pipelines: optimize data delivery, redesign for scalability, manage deployments, and drive root-cause fixes
Internship focused on developing and testing EDA flows, automation tools, and infrastructure for ASIC design within Meta’s Infrastructure ASIC organization.
Responsibilities
Contribute to the development and testing of front-end or physical design implementation flows
Help build or improve internal automation tools that increase productivity across ASIC design cycles
Write and maintain scripts that support day-to-day design flow tasks
Help document EDA flows, tool behavior, and evaluation findings for the team
Partner with internal infrastructure teams on compute grid, storage, and job scheduling efficiency
Requirements
B.S. degree program in Computer Engineering, Computer Science, Electrical Engineering, or a related field
Experienced engineer focused on developing systems and products using Generative AI in the professional domain of finance, specifically building an LLM-first knowledge stack for investment professionals.
Compensation
The base salary range for this position is $107,000-216,000 USD per year.
Responsibilities
Design, develop, and maintain robust, scalable, and secure Python-based applications and services.
Optimize Python applications for performance, memory usage, and concurrency.
Write clean, idiomatic, and well-documented Python code following PEP8 and enterprise coding standards.
Orchestrate solutions within a cloud based environment.
Apply standard methodologies for large scale application design, SOA, microservices, distributed compute, and containers.
Requirements
Bachelor’s or Master's Computer Science or relevant field
Senior Data Scientist responsible for end-to-end model development, deployment, and stakeholder communication within the Revenue Science team.
Responsibilities
Own models end to end, from design through production
Formulate and solve optimization problems for pricing and capacity decisions
Establish model monitoring and drift detection
Define experiment design and analysis across the team
Arbitrate methodological questions on high‑stakes measurement
Requirements
Master’s or PhD in a quantitative field (Computer Science, Data Science, Operations Research, Statistics, Econometrics, Industrial Engineering, Economics, or similar)
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