Competitive salary and annual discretionary bonus scheme
Responsibilities
Leading technical discovery and turning customers’ AI workloads into GPU cloud solutions
Giving Sales and BD the technical validation, scoping and proposal input that moves deals forward
Running demos, technical presentations and architecture workshops for prospects, partners and enterprise customers
Building and maintaining the technical sales library: solution briefs, battlecards, architecture patterns and FAQs
Turning competitive insight on hyperscalers and neoclouds into positioning Sales can use, and flagging risks such as capacity overcommitment before anything is promised to a customer
AI Research Engineer focused on building systems around research models, defining the AI roadmap, building retrieval, memory, and evaluation systems, and shipping them into production.
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
Seeking a TS/SCI-cleared Infrastructure Engineer to manage and optimize a high-performance compute environment supporting intensive AI/ML workloads.
Locations (2)
Springfield, Virginia, United States
Patriot Ridge, VA, United States
Compensation
$185k-$220k
Responsibilities
Manage the day-to-day operations of the customer compute cluster, including Linux operating system administration, hardware monitoring, patching, and system upgrades.
Configure, maintain, and optimize workload management and orchestration platforms, utilizing the Run:AI job scheduler.
Tune cluster performance at the hardware, operating system, and network levels.
Administer storage solutions and high-speed networking fabrics.
Support the transition to and ongoing management of an InfiniBand GPU-to-GPU network infrastructure.
Design and lead enterprise AI/ML architectures in a secure, mission-focused environment, translating emerging AI capabilities into scalable, production-ready solutions.
Compensation
$146,000 - $234,000. 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, and 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 enterprise AI/ML architectures encompassing model selection, inference infrastructure, training pipelines, MLOps, and mission application integration
Architect and deploy LLM, generative AI, Retrieval-Augmented Generation (RAG), and agentic AI solutions
Lead AI/ML solutions through the full lifecycle, from prototyping and experimentation to production deployment at enterprise scale
Design AI/ML architectures capable of operating in classified, disconnected, and air-gapped environments
Evaluate emerging AI technologies and translate new capabilities into practical, production-ready solutions
Requirements
Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related technical discipline
Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related technical discipline
Leading end-to-end program planning and execution for major AI platform initiatives, including defining technical roadmaps, milestones, dependencies, and risks.
Compensation
for US based candidates only - the base salary for this position ranges from $119,600 - $215,400/year
Responsibilities
Leading end-to-end program planning and execution for major AI platform initiatives
Materially leading the Product Development Process and the full software development lifecycle and release process for AI products
Leveraging technical expertise to lead complex projects, and managing programs from concept to market
Establishing program metrics, tracking progress, and monitoring updates
Lead the technical direction for security across fal.ai, designing and implementing secure-by-default practices for GPU compute, multi-cloud, Kubernetes, and AI systems.
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