Brookhaven National Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $104,700.00 - $150,733.00 / year.
Responsibilities
Develop, deploy, and maintain open‑source software for experiment planning, control, and data acquisition within the Bluesky/Tiled ecosystem.
Develop tools to automate and streamline the processing and analysis of powder diffraction data.
Collaborate with scientists and engineers to integrate AI algorithms into beamline operations and enable intelligent experiment steering.
Provide user‑facing support, training, and documentation while publishing and presenting results to the broader community.
Requirements
Bachelor’s in physics, chemistry, material science, biology, computational science, an engineering discipline or closely related field of study
Master’s in physics, chemistry, material science, biology, computational science, an engineering discipline or closely related field of study
Doctorate in physics, chemistry, material science, biology, computational science, an engineering discipline or closely related field of study
Nice to have: powder diffraction data analysis methods
Work and develop state of the art techniques in deep learning, graphs, machine learning, and data analytics
Provide the best AI solutions using GPUs working directly with key customers
Collaborate closely with architecture, research, libraries, tools, and system software teams to influence the design of next-generation architectures, software platforms, and programming models
Requirements
Pursuing MS or PhD from a leading University in an engineering or Computer Science related discipline
Leadership role focused on the design, development, and deployment of AI-powered products, foundation model training, and scalable-performance AI infrastructure.
Research Scientist focused on AI models, hardware acceleration, and software systems for Reality Labs' AR, VR, and wearable devices.
Responsibilities
Identify and solve multi-disciplinary ML acceleration problems spanning algorithms, model architecture, software, and hardware for AR, VR, and wearables use cases
Invent and develop state-of-the-art model compression, efficiency, and scalability techniques
Integrate proven research into production ML infrastructure for broad adoption across the organization
Post-train and optimize foundation models to deliver Pareto-optimal multimodal LLMs and GenAI models on target hardware under real-time latency, power, and memory constraints
Use models and algorithm insights to co-design next-generation hardware and system architectures
Requirements
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
PhD in Electrical Engineering, Computer Science, or equivalent experience
The AI Engineer 5 will design, develop, test, and support AI software components including foundation model training, LLM inference, agents, and multi-agent workflows to reimagine banking services for customers.
Compensation
The minimum and maximum full-time annual salaries for this role are listed below, by location... actual salary offered to any candidate at the time of hire will be reflected in candidate’s offer letter.
Responsibilities
Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
Invent and introduce state-of-the-art foundation model optimization techniques to improve performance — scalability, cost, latency, throughput.
Contribute to the technical vision and the long term roadmap of foundational AI systems.
Requirements
Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
Lead the technical evolution of data systems across Google’s merchant and communication products, building AI agents, data pipelines, and statistical frameworks to drive business impact.
The AI Engineer will design, develop, test, deploy, and support AI software components including foundation model training, LLM inference, and multi-agent workflows to reimagine how Capital One serves its customers.
Locations (3)
McLean, Virginia, United States
New York, New York, United States
San Jose, California, United States
Compensation
McLean, VA: $197,300 - $225,100 for AI Engineer 4; New York, NY: $215,200 - $245,600 for AI Engineer 4; San Jose, CA: $215,200 - $245,600 for AI Engineer 4
Responsibilities
Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Lead quantum research in continuous-variable photonics and theoretical algorithm discovery, translating models into experimental prototypes and publications.
Compensation
The projected compensation range for this position is $94,800.00-$157,200.00.
Responsibilities
Conduct foundational and applied research in quantum information science
Lead theoretical algorithm discovery and error‑correction scheme development
Prioritize experimental physics integration within photonics modalities
Translate mathematical models into experimental prototypes and technical specifications
Collaborate with academic partners and cross‑functional engineering teams
Requirements
Master’s degree in Physics, Applied Physics, Mathematics, or a related technical discipline
Nice to have: PhD in Quantum Physics, Atomic Physics, Condensed Matter, or Computer Science with a Quantum Information Science focus
Remote (Regardless of Location): $286,200 - $326,700 for Sr. Director, AI Engineer; Cambridge, MA: $314,800 - $359,300 for Sr. Director, AI Engineer; McLean, VA: $314,800 - $359,300 for Sr. Director, AI Engineer; New York, NY: $343,400 - $392,000 for Sr. Director, AI Engineer; San Francisco, CA: $343,400 - $392,000 for Sr. Director, AI Engineer; San Jose, CA: $343,400 - $392,000 for Sr. Director, AI Engineer
Responsibilities
Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
Oversee the design, development, testing, deployment, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
Contribute to the technical vision and the long term roadmap of foundational AI systems.
Requirements
Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
Nice to have: 7+ years of experience managing and leading an engineering team
Nice to have: 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms
Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
Design, develop, test, deploy, and support AI software components including foundation model training, LLM inference, agents and multi-agent workflows.
Invent and introduce state-of-the-art foundation model optimization techniques.
Own the end-to-end architecture for complex AI systems - ensuring maintainability, observability, and ethical alignment.
Define and maintain service-level objectives (SLOs) for AI reliability.
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