Lead Communications and Networking Command and Control (C2) Engineer to serve in a Deputy Trailboss capacity for Army Next Generation Command and Control (NGC2), shaping and delivering interoperable capabilities responsive to operational-unit needs.
Compensation
$147,200 - $184,000 - $220,800 Annual
Responsibilities
Serve as a senior technical integrator and NGC2 Deputy Trailboss, shaping priorities, coordinating execution, and driving delivery of integrated C2 capabilities across acquisition stakeholders, operational units, and industry partners.
Lead end-to-end architectures spanning C2 applications, data layers, tactical IP transport, resilient communications, and cloud & edge compute infrastructure to satisfy Army mission needs, including in disconnected, degraded, intermittent, or limited-bandwidth (DDIL) environments.
Develop and connect operational mission threads, technical threads, system-of-systems designs, requirements, and interface assumptions to measurable operational outcomes and testable technical objectives.
Evaluate cross-layer architecture tradeoffs involving routing, bandwidth, latency, reliability, cybersecurity, interoperability, data exchange, synchronization, and placement of workloads at the edge versus in the cloud.
Organize and participate in laboratory integration, technical demonstrations, operational exercises, and iterative fielding events; coordinate root-cause analysis, integration-risk resolution, and performance assessment across vendor solutions.
Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.
Responsibilities
Understand business use of data and stakeholder requirements to support work processes and strategic business objectives
Leverage data, software engineering, and data science techniques to create business value through data accessibility. Includes data ingestion, data preparation and analytics processing.
Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns
Responsible for normalizing and ingesting data from multiple sources.
Enable data scientists, making data available for advanced analytics models, and contributing to models.
Requirements
BS in Computer Science
Management Information Systems
Computer Engineer or related fields or equivalent experience
Develop training and post-training systems for advanced Physical AI models, including world foundation models and robot policies using NVIDIA’s GPU infrastructure.
Responsibilities
Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL).
Explore distributed parallelism, sharding, low-precision training, compute–communication overlap, and numerical consistency and efficient weight synchronization between training and inference.
Build Physical AI post-training and RL infrastructure supporting advanced training algorithms.
Connect simulation or, where applicable, real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation.
Optimize these workflows through partitioning, pipelining, data transfer, and synchronization across synchronous, asynchronous, or disaggregated execution.
Internship focused on developing and optimizing AI infrastructure for training, inference, and evaluation of advanced Physical AI models at NVIDIA’s Cosmos Lab.
Responsibilities
Develop and optimize training infrastructure for advanced Physical AI world models
Build Physical AI post‑training and RL infrastructure
Improve efficiency and scalability across training, inference, simulation, and evaluation
Analyze and optimize system performance with researchers
Define and drive the AI performance benchmarking and workload characterization strategy across CPU, GPU, APU, NPU, and heterogeneous platforms.
Benchmark AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related frameworks and runtimes.
Analyze key performance metrics, including latency, throughput, time to first token, tokens per second, memory utilization, power consumption, and performance per watt.
Profile workloads and identify bottlenecks across AI frameworks, runtimes, libraries, drivers, operating systems, firmware, memory subsystems, and hardware.
Lead complex root-cause analysis and recommend optimizations related to quantization, graph execution, kernels, memory usage, workload scheduling, and heterogeneous compute utilization.
Requirements
Bachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field
This engineer will improve end-to-end large language model (LLM) inference performance on Positron's custom AI accelerators, including our FPGA-based Atlas systems and our Titan systems built on the Asimov ASIC.
Compensation
The base salary range for this role is $200,000 – $300,000.
Responsibilities
Profile and optimize latency, throughput, utilization, and efficiency across hardware-accelerated attention, accelerator configuration, the device runtime, host software, and on-device execution.
Use performance counters, hardware and software traces, telemetry, and simulation to isolate regressions and validate fixes.
Develop and optimize distributed inference capabilities, including efficient KV cache management and data movement across heterogeneous hardware.
Build representative benchmarks and workload traces that expose bottlenecks in compute, memory, chip-to-chip interconnect, networking, scheduling, and model loading.
Partner with test engineers embedded in the team to turn performance findings into durable regression tests and release gates.
Support and contribute to evolving the Enterprise Data Strategy and Architecture, manage data movement solutions, and provide technical leadership for database and application development.
Compensation
Competitive base pay plus the ability to earn more tied to your success – the only limit is your potential
Responsibilities
Supports and contributes to evolving the Enterprise Data Strategy and Architecture
Working knowledge of traditional database systems applying ETL and data movement solutions to support target data models
Enhance and develop detailed architecture and technical design documentation
Lead solution design independently or as a team member
Maintain COTS applications including interfaces, configuration, database tuning and GUI
Requirements
College diploma or university degree in computer science or information systems
Lead data engineering initiatives for EY’s financial services clients, delivering AI and data services.
Locations (2)
Dallas, Texas, United States
San Antonio, Texas, United States
Compensation
The base salary range for this job is: New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $78,600 to $146,100 Bay Area California offices – $81,900 to $152,100 All other offices locations in the US, including Sacramento – $65,500 to $134,000
Responsibilities
Work with diverse teams to deliver AI and data services
Collaborate, mentor and coach teams
Build strong relationships with internal clients
Apply systems analysis techniques to data movement and quality
Develop customized software solutions using ETL
Requirements
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field
Nice to have: Database certifications
Nice to have: Reporting or data visualization certifications
Security and Compliance Engineer | Top Secret clearance w/SCI eligibility
Define cybersecurity, compliance, encryption, identity, audit, logging, and monitoring requirements for data ingest, application onboarding, partner access, and IPT integration requests.
Drive authorization readiness by identifying classification, information domain, releasability, transfer direction, control mapping, evidence, and approval requirements early in the intake process.
Develop security artifacts for integration architectures, interface control documents, access control plans, cryptographic protection plans, test plans, readiness packages, and authorization packages.
Assess integration risk across least privilege access, ABAC enforcement, cryptographic protections, monitoring needs, auditability, and data handling obligations.
Coordinate secure implementation with cross-domain, network, cloud, software, data, and operations teams for CDS, diode, RTB-based CDS, API, broker, streaming, and secure-transfer patterns.
Requirements
Bachelor’s degree in cybersecurity, information technology, computer science, engineering, or a related discipline
Seeking a Software Engineer with strong experience in data movement, systems development lifecycle (SDLC), and the separation of QA and production environments. The role involves technical expertise in Python, Slurm, Spark, Linux, and server infrastructure, as well as mentoring an offshore team.
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