Build and operate AWS cloud infrastructure, automation, and services for government and intelligence-community applications.
Compensation
$112,000 - $179,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Responsibilities
Design, deploy, and operate AWS infrastructure, preferably in GovCloud
Design, deploy, and operate Kubernetes, Amazon EKS, and containerized workloads
Develop and maintain Terraform, CloudFormation, or comparable infrastructure-as-code solutions
Develop and maintain GitLab CI/CD pipelines, Git workflows, and automated software delivery processes
Build and operate secure AWS and Kubernetes platforms supporting government and intelligence-community applications
Requirements
Bachelor's degree in Computer Science or related field
Master's degree in Computer Science or related field
Join an engineering team at an AI infrastructure company focused on reinforcement learning environments and evaluation. You will build systems that make quality control for training data scalable, helping improve the reliability of data used to train and evaluate AI agents.
Compensation
Base salary range: $100,000 to $170,000 USD annually. Visa sponsorship is available.
Responsibilities
Automate quality control for training data generated through the platform
Build quality systems grounded in human judgment and understanding, rather than relying heavily on large language models
Define and enforce standards for training data quality
Design experiments and metrics to evaluate agent outputs
Work with data vendors to investigate quality issues, diagnose agent failure modes, and improve data generation processes
Join a small, technical AI evaluation team building rigorous benchmarks for frontier AI agents. You will own the design and implementation of domain-specific evaluations that help research teams and customers understand how agents perform on realistic workflows.
Compensation
Base salary: $100,000 to $170,000 USD annually. Visa sponsorship is available.
Responsibilities
Design, implement, and maintain internal benchmarks for evaluating frontier agents on domain-specific tasks
Partner with subject-matter experts to translate real-world workflows into benchmark tasks and evaluation criteria
Build reliable infrastructure to run models and agents against benchmark tasks at scale
Develop metrics and analyses to assess benchmark difficulty, reliability, and failure modes
Validate whether benchmark results align with real-world evaluations and user needs
Requirements
Technical education or strong fundamentals in a technical field
Build the connection between an AI agent platform and the developer community. You will create practical resources, showcase working agent experiences, and bring developer feedback into product decisions.
Compensation
Base salary: $130,000 to $180,000 USD annually.
Responsibilities
Build and demonstrate agents across messaging and voice platforms, then publish cookbooks, templates, and example repositories.
Write documentation, quickstarts, and tutorials that help developers get started and build successfully.
Develop integrations and collaborate with partner platforms across AI and developer tools.
Grow the developer community through Discord, GitHub, office hours, hackathons, and contests.
Represent the platform at meetups and hackathons, share work publicly, and relay community feedback to the product team.
Requirements
Nice to have: Integration experience with AI and developer-tool platforms
Join a small engineering team building synthetic data systems for AI agents. You will turn domain-specific workflows into scalable training tasks, helping improve how models learn and perform in real-world applications.
Compensation
Annual base salary: $100,000 to $170,000 USD.
Responsibilities
Build pipelines that convert professional and technical workflows into realistic, structured training tasks
Collaborate with subject-matter experts to develop tasks across distinct domains
Design methods for generating diverse, realistic, and learnable synthetic tasks
Create tools to mutate, validate, and improve generated tasks
Analyze agent performance to understand task effectiveness and identify failure modes
Requirements
Technical education or equivalent technical background
Join a small engineering team at an AI company focused on reinforcement learning environments, model evaluation, and post-training data. You will tackle urgent, ambiguous technical challenges for AI labs and data vendors, while turning repeated deployment work into reusable tools.
Compensation
Base salary: $100,000 to $170,000 USD annually.
Responsibilities
Own technical deployment requests from initial triage through resolution
Diagnose ambiguous technical problems and clarify what needs to be done
Build tools and one-off pipelines to address urgent customer and partner needs
Coordinate with research, go-to-market, and other internal teams to unblock deployments
Balance speed and quality, escalating when correctness or security requires extra care
Build the intelligence layer for an AI-powered recruiting marketplace, including the recommendation, ranking, retrieval, and matching systems at the core of the product.
Compensation
Base salary is $130,000 to $170,000 annually, plus equity. Visa sponsorship is available for eligible candidates.
Responsibilities
Develop and operate recommendation, ranking, and retrieval systems.
Build LLM-powered matching features that improve candidate and role alignment.
Take features from problem definition through production, shipping at a rapid cadence.
Architect and maintain the data pipelines and services that support production models.
Partner with the founding team to translate product goals into working systems.
Help design, build, and scale the firm's enterprise data ecosystem, delivering highly scalable data engineering solutions that enable analytics, reporting, operational efficiency, and AI-driven capabilities across Ares' global business.
Compensation
$210,000 - $230,000
Responsibilities
Define and own the enterprise Data Ingestion & Integration strategy and multi-year roadmap
Establish standards, governance, and best practices for enterprise-wide data movement and integration
Build and lead high-performing engineering teams responsible for ingestion, integration, and data connectivity services
Drive adoption of modern, event-driven, API-first, and cloud-native integration patterns
Partner with product teams to prioritize strategic data initiatives aligned to business growth objectives
Responsible for leading the enterprise data engineering organization responsible for transforming raw data into trusted, consumable, and business-ready data products.
Compensation
$210,000 - $230,000
Responsibilities
Define and execute the enterprise Data Engineering and Data Products strategy
Establish engineering standards, frameworks, and best practices for data transformation and product development
Build and lead high-performing teams of data engineers, platform engineers, and data product developers
Drive the transition from project-based data delivery to product-centric data management
Partner with business and technology leaders to prioritize strategic data products and capabilities
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