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3 jobs found

Today

AI Engineer 4 (Vision model (VLM) customization experience)

Capital One New York, NY

New York, NY

Today

$215k - $246k/yr Full time Agentic Ai Ai Agent Ai Guardrails

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Job Description

This role focuses on customizing and deploying AI systems for production at scale, including foundation model work and vision model (VLM) customization experience. You will help shape architecture, reliability, and evaluation practices while optimizing training and inference for performance, cost, and throughput.

Location

New York, NY (onsite)

Compensation

USD 215,200 - 245,600 per year

Experience Level

Minimum 4 years of relevant experience.

Team

The Intelligent Foundations and Experiences (IFX) team is central to delivering Capital One’s vision for AI. The team partners across the company to advance science and AI engineering, builds and deploys proprietary solutions, and supports teams in adding AI capabilities in responsible and scalable ways.

Key Responsibilities

Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that support how associates work and how customers interact with Capital One.
Design, develop, test, deploy, and support AI software components across foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Apply a broad set of Open Source and SaaS AI technologies, including AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and others.
Develop and introduce state-of-the-art foundation model optimization techniques to improve scalability, cost, latency, and throughput for production AI systems.
Contribute to technical direction and the long-term roadmap for foundational AI systems at Capital One.
Own end-to-end architecture for complex AI systems, with focus on maintainability, observability, and ethical alignment.
Define and maintain service-level objectives (SLOs) for AI reliability, including latency, uptime, and model performance drift.
Collaborate with infrastructure engineering to optimize GPU/TPU utilization and improve model inference pipeline performance.
Lead cross-functional technical reviews for new AI deployments, including security, data governance, and compliance standards.
Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation.

Required Qualifications

Bachelor’s Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields, plus at least 4 years of experience developing AI and ML algorithms or technologies; or a Master’s Degree plus at least 2 years of experience.
At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
Experience leading AI system development with tradeoffs across cost, latency, throughput, and accuracy.
6 years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
Experience designing, developing, delivering, and supporting AI services.
Experience developing AI and ML algorithms or technologies (e.g., LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang.
Experience building and applying state-of-the-art optimization techniques for training and inference software to improve hardware utilization, latency, throughput, and cost.
Experience building agentic AI systems and agentic workflows.
Proficiency designing distributed systems for model training, evaluation, and online inference at petabyte scale.
Experience defining AI model governance processes, including producibility, lineage tracking, and automated retention schedules.
Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms.

Technologies

AWS Ultraclusters
Huggingface
VectorDBs
PyTorch
AWS, Google Cloud, Azure
Python, Go, Scala, CUDA, Java, C++, C#, Golang
GPU, TPU
LLM Inference, Similarity Search, Guardrails, Memory

Benefits

Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being.
Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

Additional Information

This role is expected to accept applications for a minimum of 5 business days.
No agencies please.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws.
Capital One promotes a drug-free workplace.
If you need an accommodation to apply, contact Capital One Recruiting at 1-800-304-9102 or RecruitingAccommodation@capitalone.com.
For technical support or questions about Capital One’s recruiting process, email Careers@capitalone.com.
Capital One does not provide, endorse, or guarantee third-party products, services, educational tools, or other information available through this site.

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7d ago

Staff Machine Learning Engineer

Unity Technologies SF Mountain View, CA

Mountain View, CA

7d ago

$172k - $284k/yr Full time Ai Ml Ai Model Training Artificial Intelligence

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Job Description

Unity Technologies SF is hiring a Staff Machine Learning Engineer to build production-grade AI for game experiences with a focus on computer vision and multi-modal modeling.

Responsibilities

Set technical vision and roadmap for computer vision and multi-modal AI models, covering transformers, diffusion models, vision-language models, and JEPA-style generative architectures
Design and implement models for image and video understanding and generation, including segmentation, detection, and dense prediction
Develop multi-modal reasoning over images, text, and 3D inputs
Make architecture, training, data pipeline, and evaluation trade-offs balancing quality, capability, latency, and cost across cloud, server, and on-device targets
Drive research-to-production delivery: training, fine-tuning, distillation, export, and serving for deployment scenarios from cloud GPUs to efficient on-device inference
Partner with research scientists to translate novel CV and multi-modal architectures into deployable, well-engineered implementations
Build scalable multi-modal inference systems that ingest diverse inputs (images, video, text, primitives, and metadata) and produce outputs ranging from semantic predictions to pixel-level generation
Monitor and adopt field breakthroughs including vision-language pretraining and alignment, efficient diffusion approaches (consistency models, flow matching), efficient attention (FlashAttention, linear-attention variants), and vision tokenization/representation learning
Where needed for latency or device constraints, apply compression and optimization such as compression, quantization, pruning, and knowledge distillation, and integrate runtimes like TensorRT, ONNX Runtime, CoreML, and TFLite
Lead and mentor ML engineers, establishing engineering best practices, code review standards, and rigorous benchmarking and evaluation methodology
Collaborate with research, platform engineering, product managers, and runtime teams to align ML capabilities to product roadmaps and target-platform constraints
Define and enforce measurement practices using KPIs for model quality, accuracy, latency, memory, and cost

Requirements

6+ years of ML engineering with strong depth in computer vision and/or multi-modal modeling
Production experience with transformer-based and diffusion-based vision models (examples: ViT, CLIP/SigLIP-style encoders, Stable Diffusion, DETR/SAM-style architectures)
End-to-end model lifecycle experience including data curation, training and fine-tuning, evaluation, and serving at scale
Familiarity with efficient attention, diffusion samplers, multi-modal fusion, and vision-language alignment methods
Strong Python skills and modern deep-learning tooling such as PyTorch, plus solid software engineering fundamentals
Proven technical leadership: setting direction, influencing cross-functional partners, and growing engineers

Technologies

Python, PyTorch
TensorRT, ONNX Runtime, CoreML, TFLite
FlashAttention
ViT, CLIP, SigLIP
Stable Diffusion
DETR, SAM

Benefits

Comprehensive health, life, and disability insurance
Commute subsidy
Employee stock ownership
Competitive retirement/pension plans
Generous vacation and personal days
Support for new parents through leave and family-care programs
Office food snacks
Mental Health and Wellbeing programs and support
Employee Resource Groups
Global Employee Assistance Program
Training and development programs
Volunteering and donation matching program

Additional information

Location: Mountain View, CA (onsite)
Salary (USD per year): USD 172,200 - 283,900
Zone A: $218,400 - $283,900
Zone B: $194,100 - $252,300
Zone C: $172,200 - $223,900
Beyond base salary, the role may be eligible for equity awards and participation in company incentive plans (including annual discretionary bonuses or sales commissions)
Final offer depends on geographic location, relevant experience, professional background, and skill set

You might also have

Experience with world-model, video-generation, or neural rendering pipelines (NeRF, 3DGS, or similar)
Experience deploying models to constrained or on-device targets, including quantization (INT8/INT4/FP16), pruning, distillation, and runtimes such as CoreML, TFLite, ONNX
Familiarity with mobile SoC accelerators (Apple Neural Engine, Qualcomm Hexagon/Adreno, ARM Mali) or compiler stacks such as MLIR, TVM, or XLA
Contributions to open-source ML frameworks or peer-reviewed CV/ML research publications
Background in real-time graphics or game engine pipelines (Metal, Vulkan, OpenGL ES)

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1mo ago

Machine Learning Engineer

Johns Hopkins Applied Physics Laboratory (APL) Laurel, MD

Laurel, MD

1mo ago Closed

$100k - $245k/yr Full time Ai Engineer Ai Ml Artificial Intelligence

This position is no longer accepting applications (closed Sep 22, 2026).

Job Description

Join the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, MD (onsite) as a Machine Learning Engineer contributing across the full lifecycle of ML algorithm development and implementation. This role supports national defense non-kinetic systems, where you will apply modern AI methods alongside real development pipelines, simulation environments, and intelligent decision-making systems.

Compensation: $100,000 to $245,000 per year. Experience: at least 2 years. Education: Bachelor’s degree in Mathematics, Physics, Engineering, Computer Science, or a related field.

What you’ll do

Design, implement, and evaluate advanced machine learning algorithms to address real-world planning, perception, coordination, and control challenges for national defense.
Develop software pipelines that connect data streams, simulation environments, and intelligent decision-making algorithms.
Apply cutting-edge AI technologies and concepts, including deep reinforcement learning, foundation models, large language models, convolutional/recurrent/graph neural networks, computer vision, and physics-based modeling and simulation tools.
Collaborate with scientists and engineers within your group and across APL to advance solutions.
Engage with sponsors to communicate proposed concepts, solutions, and analysis.

What you bring

Bachelor’s degree in Mathematics, Physics, Engineering, Computer Science, or a related field.
At least 2+ years of experience in machine learning and data science.
At least 1 year of hands-on experience applying and/or developing ML algorithms using common libraries such as PyTorch or TensorFlow.
Strong foundational knowledge in at least two areas: classification, clustering, deep learning, reinforcement learning, computer vision (object detection and visual tracking), multi-agent systems, or optimization/control theory.
Demonstrated experience using version control software such as Git.
Strong communication skills, both verbal and written.
Ability to obtain an Interim Secret clearance by your start date and ultimately a Secret clearance. If selected, you will be subject to a government security clearance investigation and must meet eligibility requirements, including U.S. citizenship.

Technologies you’ll work with

PyTorch, TensorFlow, Git
Deep reinforcement learning, foundation models, large language models
Convolutional neural networks, recurrent neural networks, graph neural networks
Computer vision
Physics-based modeling and simulation tools

Nice to have

MS in Mathematics, Physics, Engineering, Computer Science, or a related field.
5+ years of experience designing and implementing AI/ML algorithms for a variety of datasets.
Proven experience applying state-of-the-art deep learning techniques to solve distributed resource allocation problems.
Hands-on experience building computer vision pipelines for detection, tracking, segmentation, or multi-modal sensor fusion.
Experience with modeling and simulation platforms such as AFSIM, Blender, Unity, or Unreal.
Comfort working in high performance computing environments (GPU/CPU clusters).
Proficiency in one or more: multi-agent reinforcement learning, geometric deep learning, multi-modal sensor fusion, agentic AI.
Track record of writing deployable, production-level code (Python, C/C++) for real-world applications.

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Computer Vision Models Data Jobs

AI Engineer 4 (Vision model (VLM) customization experience)

Staff Machine Learning Engineer

Machine Learning Engineer

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