Computer Vision Model Deployment

Computer Vision Model Deployment Data Jobs

3 jobs found

Today

Deep Learning Data Scientist II

Spectral MD Inc Dallas, TX

Dallas, TX

Today

Full time Artificial Intelligence Computer Vision Model Deployment Data Pipeline

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Job Description

Spectral MD Inc is hiring a Deep Learning Data Scientist II to support research and development of AI and ML algorithms for computer vision in healthcare. This onsite role in Dallas, TX focuses on building deep learning models, preserving data quality, and improving performance for efficient deployment, while partnering with clinical and internal stakeholders to deliver practical solutions.

What you’ll work on

Research, develop, evaluate, and enhance machine learning and deep learning algorithms for computer vision, turning AI research progress into healthcare-ready capabilities.
Build and optimize model deployment pipelines to support efficient, scalable, hardware-optimized inference.
Apply model optimization techniques to improve computational efficiency and inference performance while maintaining accuracy.
Maintain data quality and integrity across the data lifecycle to enable robust algorithm development, training, and evaluation.
Collaborate with clinical partners and internal stakeholders to translate clinical and business needs into appropriate AI/ML solutions.
Independently manage assigned R&D projects from problem formulation through implementation, evaluation, and delivery.
Ensure reproducibility using clear documentation, version-controlled workflows, and effective knowledge sharing.
Stay current with advances in AI/ML and computer vision, evaluating applicability to ongoing R&D.
Communicate technical methods, results, and recommendations to both technical and non-technical audiences.

Key qualifications

M.S. or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or a related quantitative field.
Minimum two years of relevant research or professional experience in computer vision.
If holding a master’s degree, required experience must be professional experience gained after completion of the degree.
Strong Python programming skills and experience building maintainable, reproducible ML code.
Strong foundation in modern deep learning architectures, including vision transformers, for image classification, object detection, and semantic segmentation.
Proficiency with deep learning frameworks and libraries, including PyTorch and TensorFlow.
Strong understanding of AI/ML evaluation metrics, benchmarking methodologies, and best practices for rigorous computer vision model validation.
Solid understanding of model optimization techniques such as pruning, quantization, and mixed-precision inference to improve efficiency and inference performance.
Working knowledge of deployment and inference tools including ONNX, LibTorch, and TensorRT.
Self-driven ability to navigate ambiguous research problems with incomplete data and still deliver high-quality results.
Strong written and verbal communication skills, including preparation of technical documentation, reports, and scientific manuscripts.

Preferred qualifications

Experience with multispectral and/or hyperspectral imaging data.
Experience with generative AI models for medical image synthesis and simulation.
Experience applying AI/ML methodologies to healthcare or biomedical applications, with demonstrated contributions to successful research, clinical, or product outcomes.

Technologies you’ll use

Python, PyTorch, TensorFlow
ONNX, LibTorch, TensorRT

Work setup

Location: Dallas, TX (onsite)
Travel: N/A
Physical requirements: Prolonged periods of sitting at a desk and working on a computer.

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Today

Lead Machine Learning Engineer, Human Sensing

Apple Seattle, WA

Seattle, WA

Today

$175k - $309k/yr Full time Manager Ai Ml Artificial Intelligence

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Job Description

Apple’s Human and Object Understanding (HOUr) team is hiring a Lead Machine Learning Engineer (Technical Lead) to set technical direction for a multimodal Human Sensing team in Seattle, WA.

Partner with engineering management as the primary technical lead to define project scope, technical milestones, and roadmap execution
Set KPI targets and quality benchmarks across demographics, environmental conditions, and device use cases
Define dataset collection, annotation, and curation strategy with the Data team to reduce model blind spots
Architect and lead the team’s evaluation framework and benchmarking pipelines (custom metrics, evaluation scripts, automated tooling)
Stress-test models against production scenarios through evaluation and benchmarking automation
Lead failure mode analysis: root-cause investigation and edge-case discovery to drive model and data iterations
Coordinate day-to-day technical execution across Evaluation, Integration, and Data Operations teams
Drive model optimization in partnership with integration and other partner teams
Train, fine-tune, and run experiments with state-of-the-art vision architectures when needed for research and hypothesis validation
Maintain the team’s core codebase: author and review PRs, uphold engineering hygiene, and enable rapid iteration velocity
Communicate technical strategy, performance trade-offs, and progress to stakeholders and senior leadership
Guide and mentor junior and mid-level engineers
Stay current with machine learning trends and best practices across multimodal foundation models, computer vision, and natural language understanding

Requirements

Master’s or Ph.D. in Computer Science, Computer Engineering, or related field (or equivalent practical experience)
6+ years of industry experience in Computer Vision and Machine Learning
Proven Technical Lead or Staff-level experience driving project scoping, KPI setting, and technical initiatives across cross-functional teams
Expertise in evaluating complex ML systems, defining benchmarking methodologies, and conducting deep-dive failure analysis
Ability to coordinate engineering teams and mentor peers while partnering closely with management on roadmap execution
Strong ownership mindset, attention to detail, and agility in fast-evolving research environments
Deep proficiency in Python and PyTorch, including hands-on experience writing clean, maintainable code and managing shared repositories

Technologies

Python, PyTorch
Core ML
Quantization-aware training, knowledge distillation
Latency profiling, quantization, pruning
Multimodal foundation models, computer vision, natural language understanding
Face recognition, identity re-identification (ReID)
Foundation vision models
Large-scale Vision-Language Models (VLMs), large language models (LLMs)
Multimodal large language models (LLMs), large-scale vision-language models (VLMs)

Benefits

Comprehensive medical and dental coverage
Retirement benefits
Range of discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary bonuses or commission payments (may be eligible)
Relocation (may be eligible)
Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
Eligible for discretionary restricted stock unit awards
Can purchase Apple stock at a discount with voluntary participation in the Employee Stock Purchase Plan

Preferred Qualifications

Domain knowledge in face recognition, identity re-identification (ReID), biometrics, or visual human sensing (pose, expression, human-object interaction)
Experience collaborating with Data Collection & Annotation teams on collection protocols and active learning datasets
Experience with on-device model optimization (quantization-aware training, knowledge distillation, Core ML conversion, latency profiling)
Experience with foundation vision models or large-scale Vision-Language Models (VLMs)
Hands-on experience training and scaling multimodal LLMs or large-scale VLMs
Experience with on-device ML, model optimization (knowledge distillation, quantization, pruning), or production-grade ML pipelines
Research and innovation background shown through publications, patents, or impactful software developments

Pay & Benefits

Base pay range: $175,000 to $308,500 per year
Base pay depends on skills, qualifications, experience, and location
Apple also offers eligibility for discretionary restricted stock unit awards and an Employee Stock Purchase Plan discount (if voluntarily participating)
Note: Benefits, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program

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3d ago

Staff Machine Learning Engineer

Unity Technologies SF Mountain View, CA

Mountain View, CA

3d 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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Computer Vision Model Deployment Data Jobs

Deep Learning Data Scientist II

Lead Machine Learning Engineer, Human Sensing

Staff Machine Learning Engineer

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