computer vision engineer

MASTER-WORKS

<div>Responsibilities<ul><li>
Solve real-world Computer Vision problems using cutting-edge techniques.</li><li>
Design and implement robust, industry-grade algorithms.</li><li>
Utilize OpenCV, Python, and deep learning frameworks for model training.</li><li>
Work with deep learning libraries such as Keras, TensorFlow, and PyTorch.</li><li>
Develop integrations with internal and external microservices.</li><li>
Implement deployment practices using Docker, Kubernetes, and model compression techniques.</li><li>
Research the latest technologies and develop proof of concepts (POCs).</li><li>
Build and train state-of-the-art deep learning models for:</li><li><ul><li>
Object Detection (Mandatory)</li><li>
Segmentation</li><li>
Classification</li><li>
Object Tracking</li><li>
Visual Style Transfer</li><li>
Generative Adversarial Networks (GANs)</li></ul></li><li>
Collaborate with researchers and engineers to develop and deploy Computer Vision solutions.</li><li>
Plan and execute Computer Vision research projects, defining scope, objectives, and deliverables.</li><li>
Provide specialized technical and scientific research support for ongoing and new AI initiatives.</li></ul> 

Requirements 
Required Skills<ul><li>
C++ (Mandatory)</li><li>
Python (Mandatory)</li><li>
Image Processing (Mandatory)</li><li>
Computer Vision (Mandatory)</li><li>
Deep Learning (Mandatory)</li><li>
Object Detection</li><li>
Machine Learning</li><li>
Pattern Recognition</li><li>
Artificial Intelligence (AI)</li><li>
Data Science</li><li>
Generative Adversarial Networks (GANs)</li><li>
Flask</li><li>
SQL</li></ul></div>

SDE-1 (Computer Vision Engineer)

Quantum HR

<div>About Us
Quantum HR is a premier human resources consulting firm dedicated to connecting exceptional talent with leading organizations worldwide. We specialize in providing bespoke recruitment solutions, leveraging deep industry insights and a global network to help companies build high-performing teams by matching them with professionals who truly fit their needs and culture. 
 
About the Role
We are hiring a Machine Learning Engineer to design, build, and deploy advanced ML models powering Digified&rsquo;s digital identity verification and contracting platforms. 
You will work on real-world, high-impact problems in computer vision, biometrics, OCR, fraud detection, and NLP, delivering production-grade ML systems integrated into APIs and enterprise workflows.
This is a hands-on engineering role focused on taking models from research to scalable, secure, and compliant production environments. 
 
Key Responsibilities 
<ul><li>
Develop and optimize ML models for face recognition, liveness detection, OCR, fraud detection, and NLP-based document understanding
</li><li>Build and maintain production-grade ML pipelines and APIs for real-time inference
</li><li>Deploy and scale models using MLOps best practices (CI/CD, versioning, monitoring, retraining)
</li><li>Optimize model performance for latency, accuracy, and reliability in production environments
</li><li>Design and manage high-quality datasets with proper annotation and data governance
</li><li>Monitor model performance (drift, accuracy, false acceptance/rejection rates)
</li><li>Collaborate with backend, mobile, DevOps, and product teams to integrate ML systems
</li><li>Ensure secure, compliant ML practices aligned with regulatory and data privacy standards
</li><li>Conduct research and propose improvements in model architectures and system performance</li></ul> 

Requirements 
<ul><li>
3&ndash;7 years of experience in Machine Learning, Deep Learning, or Computer Vision
</li><li>Strong Python programming skills
</li><li>Experience with PyTorch and/or TensorFlow/Keras
</li><li>Strong background in at least one of: Computer Vision, NLP, or Fraud Detection
</li><li>Experience with ML deployment (FastAPI, Flask, Docker, Kubernetes)
</li><li>Understanding of MLOps principles (model versioning, monitoring, retraining pipelines)
</li><li>Solid foundation in mathematics (linear algebra, probability, statistics)
</li><li>Experience working with production ML systems 
 
Nice to Have 
</li><li>
Experience with face recognition, OCR, or liveness detection systems
</li><li>Exposure to Vision Transformers, Siamese Networks, CRNNs, or LLM-based systems
</li><li>Experience with distributed training or GPU acceleration
</li><li>Familiarity with MLflow or experiment tracking tools
</li><li>Knowledge of identity standards (FIDO, NIST 800-63-3)
</li><li>Background in fintech, regtech, or anti-fraud systems
</li><li>Experience with trust &amp; safety or government-grade systems</li></ul> 

Benefits 
<ul><li>
Work on cutting-edge ML systems in digital identity and fraud prevention
</li><li>High-impact role with production ownership from research to deployment
</li><li>Exposure to enterprise-grade AI systems used in regulated environments
</li><li>Strong engineering culture with cross-functional collaboration
</li><li>Opportunity to work on advanced CV/NLP/biometric systems at scale
</li><li>Career growth in a fast-scaling AI-driven product company</li></ul></div>

ML Engineer

Apple

Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide! 
 
As part of Apple's AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. The Data and Machine Learning Innovation (DMLI) team is looking for Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges. 
 
Description 
 
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state of the art research in ML to tackle complex data problems. The solutions you develop will significantly impact Apple Intelligence, future Apple products and the broader ML development ecosystem. 
 
You will work with a multidisciplinary team to actively participate in Apple Intelligence&rsquo;s data-model co-design and co-develop practice. Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for Apple Intelligence foundation models at Apple. You will also be responsible to build robust model evaluation pipelines, integral to the continuous improvement and assessment of Apple Intelligence foundation models. 
 
Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. 
 
Your work may span a variety of directions, including but not limited to: 
 
Develop and implement techniques for creating high-quality synthetic datasets across a variety of domains, including vision, text, and audio data. 
 
Innovate and experiment with new approaches for synthetic data generation to improve the diversity, realism, and representativeness of datasets. 
 
Collaborate with multi-functional teams to understand data requirements and ensure that synthetic datasets are optimized for training foundation models. 
 
Crafting and implementing semi-supervised, self-supervised representation learning techniques for growing the power of both limited labeled data and large-scale unlabeled data. 
 
Develop pipelines and tools to automate synthetic data generation for large-scale AI experiments. 
 
Stay updated with the latest research and industry trends in synthetic data generation, foundational model training, and large-scale data engineering. 
 
Preferred Qualifications 
 
3+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality. 
 
Strong publication record in relevant conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, , etc) 
 
Strong problem-solving and communication skills. 
 
Ph.D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas 
 
Minimum Qualifications 
 
Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning. 
 
Deep understanding in multi-modal foundation models. 
 
Staying on top of emerging trends in generative AI and multi-modal LLM. 
 
Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax. 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

AIML - Machine Learning Engineer, Data and ML Innovation

We&rsquo;re starting to see the incredible potential of multimodal foundation and large language models, and many applications in the computer vision and machine learning domain that previously appeared infeasible are now within reach. We are looking for a highly motivated and skilled Machine Learning Integration Engineer to join our team in the Video Computer Vision group and help us enable that potential for realtime human understanding on Apple devices. The Video Computer Vision org has pioneered human-centric real-time features such as FaceID, FaceKit, and Gaze and Hand gesture control which have changed the way millions of users interact with their devices. We balance research and product requirements to deliver Apple quality, pioneering experiences, innovating through the full stack, and partnering with HW, SW and AI teams to shape Apple's products and bring our vision to life. 
 
Description 
 
As part of the Video Computer Vision (VCV) team, you will support our ML development and continuously improve our features. Working closely with ML algorithms engineers, data scientists and quality assurance teams, you&rsquo;ll help deploy state-of-the-art computer vision technologies on Apple devices, balancing performance with the compute and power constraints of on-device inference.&quot;,&quot;responsibilities&quot;:&quot;Develop applications and tools for algorithm evaluation, prototyping, and technology demonstrations. 
 
Support teams across Apple by integrating ML and CV components into production systems. 
 
On-device integration of CV/ML algorithms. 
 
Preferred Qualifications 
 
Experience with CoreFoundation, RealityKit and CoreML. 
 
Hands-on experience with CI/CD pipelines and practices. 
 
Experience with live camera streaming applications: Understanding of real-time video pipelines, image transformations, and rendering loops. 
 
Familiarity with common computer vision techniques (e.g., object detection, segmentation, tracking, pose estimation), sequence models for real-time inference and LLMs optimized for on-device performance. 
 
Minimum Qualifications 
 
Bachelor's degree in Computer Science or related discipline, and 2 years relevant industry experience. 
 
Strong foundational knowledge in Computer Science. 
 
Extensive programming experience in Python, Swift, C++. 
 
Experience working with PyTorch. 
 
Experience with machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment. 
 
Foundational understanding of machine learning: ML algorithms and development pipelines, with the ability to work effectively with ML practitioners and integrate ML components into production systems. 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

On-Device Machine Learning Integration Engineer

Petlibro

<div>
 
 Benefits:
 
 
 
 <ul>
 <li>
 401(k)
 </li>
 <li>
 401(k) matching
 </li>
 <li>
 Competitive salary
 </li>
 <li>
 Dental insurance
 </li>
 <li>
 Health insurance
 </li>
 <li>
 Opportunity for advancement
 </li>
 <li>
 Paid time off
 </li>
 <li>
 Relocation bonus
 </li>
 <li>
 Vision insurance
 </li>
 <li>
 Wellness resources
 
 
 
 </li>
 </ul>
 
 Petlibro | San Jose, CA
 
 
 
 
 About Petlibro
 
 
 
 Petlibro is a design-thinking company creating products that nurture the intertwined lives of pets and their people. We launched with a philosophy that good design, in form and in function, can make a difference. Petlibro innovates with the latest technology to solve everyday problems for modern pet parents and revolutionize how we care for our pets.
 
 
 Since 2019, Petlibro has grown into one of the best-selling pet tech brands globally. From smart feeders with app insights to ultra-filtered automatic fountains to pet-health-focused smart apps, our products are engineered to magnify the bond between your pet and you. We are now building AI-powered products and services for modern pet care.
 
 
 
 Job Summary
 
 
 
 We are looking for an AI Engineer to design, train, and deploy AI models that power intelligent pet care and animal monitoring products. You will work across the full model lifecycle, from data curation and fine-tuning to production serving and on-device deployment. The role spans computer vision, vision-language models (VLMs), large language models (LLMs), and agentic AI systems, with a strong focus on animal behavior understanding and multimodal reasoning.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Fine-tune LLMs and VLMs for pet care and animal behavior domains using modern post-training methods (SFT, DPO, GRPO/DAPO)
 </li>
 <li>
 Develop and evaluate computer vision models for animal detection, pose estimation, activity recognition, and health monitoring
 </li>
 <li>
 Build multimodal AI pipelines that combine video, audio, weight sensor data, and other sensor modalities for real-time animal behavior analysis
 </li>
 <li>
 Design and operate model serving infrastructure with high throughput and low latency (vLLM, TensorRT-LLM)
 </li>
 <li>
 Implement RAG systems and agentic workflows for domain-specific knowledge retrieval and automated decision-making
 </li>
 <li>
 Optimize models for on-device / edge deployment using quantization (GPTQ, AWQ, INT4/INT8), distillation, etc.
 </li>
 <li>
 Curate and manage training datasets including synthetic data generation and annotation pipelines for animal imagery and video
 </li>
 <li>
 Develop evaluation frameworks and benchmarks specific to animal AI tasks (behavior classification accuracy, false alert rates, etc.)
 </li>
 <li>
 Collaborate with firmware and embedded engineers to ship models on resource-constrained hardware
 </li>
 <li>
 Monitor model performance in production, set up drift detection, and maintain model versioning and rollback processes
 
 
 
 </li>
 </ul>
 
 Qualifications &amp; Skills
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, AI/ML, Electrical Engineering, or a related field
 </li>
 <li>
 2+ years of hands-on experience training and deploying ML models in production
 </li>
 <li>
 Strong proficiency in Python; familiarity with C++ for performance-critical paths
 </li>
 <li>
 Deep experience with PyTorch and the Hugging Face ecosystem (Transformers, PEFT, TRL, Datasets)
 </li>
 <li>
 Practical experience with the modern post-training pipeline: SFT, preference optimization, and RL-based methods
 </li>
 <li>
 Understanding of modern model architectures: Mixture of Experts, long-context models, multimodal encoders
 </li>
 <li>
 Experience with inference optimization: quantization, speculative decoding, KV-cache management, batching strategies
 </li>
 <li>
 Familiarity with MLOps tooling: experiment tracking (W&amp;B, MLflow), model registries, CI/CD for ML
 </li>
 <li>
 Knowledge of cloud GPU infrastructure (AWS, GCP) and cost-efficient training/serving strategies
 </li>
 <li>
 Strong problem-solving skills and ability to work in a fast-paced, cross-functional environment
 </li>
 <li>
 Background in animal behavior, veterinary science, or bioinformatics is a strong plus
 
 
 
 </li>
 </ul>
 
 Nice to Have
 
 
 
 <ul>
 <li>
 Experience with video understanding models and temporal reasoning over long sequences
 </li>
 <li>
 Hands-on experience building agentic AI systems or tool-using LLM workflows
 </li>
 <li>
 Experience with structured outputs, function calling, and LLM-as-judge evaluation patterns
 </li>
 <li>
 Familiarity with on-device ML frameworks (Core ML, TensorFlow Lite, ONNX Runtime, ExecuTorch)
 </li>
 <li>
 Experience with synthetic data generation for training data augmentation
 </li>
 <li>
 Understanding of distributed training (FSDP, DeepSpeed) for large-scale model training
 </li>
 <li>
 Knowledge of AI safety, alignment techniques, and responsible AI practices
 </li>
 <li>
 Experience with Docker, Kubernetes, and production deployment pipelines
 
 
 
 </li>
 </ul>
 
 Why Join Us
 
 
 
 <ul>
 <li>
 Work directly on AI that improves animal welfare and pet care. Your models have real-world impact on pets and their owners
 </li>
 <li>
 Full-stack AI role, from research and training to production serving and on-device deployment
 </li>
 <li>
 Access to GPU compute and cloud infrastructure for experimentation and training
 </li>
 <li>
 Collaborative team that values shipping over slides
 </li>
 <li>
 Competitive salary, equity, and benefits
 
 
 
 </li>
 </ul>
 We're building the future of intelligent pet care. If you want to push the boundaries of multimodal AI and apply it to a domain that genuinely matters, we want to talk.
</div>

AI Engineer - AI Department

Cplus Soft (Pvt) Ltd

We are hiring a Senior AI Engineer to design and build a next-generation agentic OCR and document processing system. You will own the end-to-end development of a multi-agent pipeline that extracts, validates, and delivers structured data from business documents using AI.This role goes beyond traditional OCR &mdash; you will architect intelligent, self-healing systems that improve accuracy and reduce manual review through targeted AI workflows.Key Responsibilities<ul><li>Design and build a multi-layer AI agent pipeline for document processing</li><li>Develop OCR extraction, classification, and schema mapping systems</li><li>Implement confidence scoring and automated validation logic</li><li>Build self-healing workflows for low-confidence data extraction</li><li>Orchestrate workflows using LangGraph and AWS Step Functions</li><li>Develop and integrate MCP-based AI tools and APIs</li><li>Optimize OCR engines for accuracy, cost, and performance</li><li>Build scalable infrastructure using AWS, Docker, and Terraform</li><li>Ensure system reliability with monitoring, testing, and observabilityRequirements</li><li>5+ years of Python development (production-level)</li></ul>Good to Have<ul><li>Computer vision / OpenCV experience</li><li>Document classification &amp; schema mapping</li><li>Human-in-the-loop (HITL) systems</li><li>LLM observability tools (LangSmith, Arize, etc.)</li><li>Experience with logistics, manufacturing, or document-heavy industries</li><li>2+ years working with LLMs and AI systems</li><li>Experience with LangChain / LangGraph or similar frameworks</li><li>Strong hands-on experience with AWS (Bedrock, Lambda, S3, ECS, Step Functions)</li><li>Solid understanding of OCR and document processing systems</li><li>Experience building APIs (FastAPI) and working with PostgreSQL / OpenSearch</li><li>Familiarity with Docker, Terraform, and cloud infrastructure</li><li>Strong English communication skills (B2+)</li></ul>Application Question(s):<ul><li>What is your current and expected salary?</li><li>Can you join on an immediate basis?</li></ul>Work Location: In person

Senior AI Engineer – Agentic OCR & Document Intelligence

Mill

<div><div>Mill is a waste prevention technology company reimagining what it means to eliminate waste, starting with food. We build smart systems and infrastructure for homes, businesses, and municipalities that transform food scraps from landfill-bound waste into valuable resources, including chicken feed. Tens of thousands of Mill's residential food recyclers are already helping households divert millions of pounds of food scraps every year, paving the way for our upcoming launch of Mill Commercial&mdash;the industry's first end-to-end solution for managing, understanding, and preventing food waste in commercial environments (e.g. grocery, restaurants, food services). At Mill, we are passionate about building easy-to-use, beautifully designed technologies that keep food in the food system and out of landfills.</div><h2 class="jobSectionHeader">
About the Role</h2>

We're hiring an AI Engineer to work on the AI core of Mill Commercial &mdash; the computer vision and agentic systems that turn a stream of food waste into operational intelligence for commercial kitchens. Mill Commercial integrates a camera and onboard compute directly into our high-capacity food recycler; models running on the edge identify, classify, and quantify food scraps at the point of generation, and our vision pipeline turns that signal into procurement and operational guidance for large food service operators.

You'll join a small AI team, building the data and training pipeline that produces our edge CV models, designing the cloud-side evaluation that tells us whether those models are good enough to ship, and helping build the agentic, LLM-driven product features that turn raw waste data into customer-facing insights and recommendations. This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation model, prompting a VLM, and wiring an agent into a product feature.
<h2 class="jobSectionHeader">
What You'll Do</h2>
<ul><li>Build and manage the end-to-end ML training pipeline: data ingestion from deployed kitchen units, ground truth generation, annotation tooling (including foundation-model-assisted labeling), training, evaluation, and retraining cycles.</li>
<li>Train and evaluate segmentation, classification, and mass-estimation models for the Mill Commercial camera pipeline &mdash; from prompting foundation models to fine-tuning ConvNets and VLMs.</li>
<li>Build the cloud-side evaluation harness that tells us how our shipped edge models are actually performing in the field &mdash; automated, reproducible, and aligned to product accuracy targets across food types, kitchen environments, and deployment configurations.</li>
<li>Own MLOps: reproducible training, experiment tracking, model versioning, and automated evaluation against product-defined accuracy targets.</li>
<li>Export and validate models for deployment to edge devices, working closely with the edge team on optimization, quantization, and integration.</li>
<li>Help design and build the LLM- and agent-powered product features that consume waste characterization data and turn it into customer-facing recommendations &mdash; purchasing suggestions, anomaly explanations, operational nudges. Define how agents call tools, ground in customer data, and stay reliable in production.</li>
<li>Analyze failure cases systematically &mdash; unfamiliar food classes, novel kitchen environments, challenging lighting and clutter conditions &mdash; and drive the data and modeling decisions that close accuracy gaps.</li>
</ul><h2 class="jobSectionHeader">What We're Looking For</h2>
<ul><li>Strong fundamentals in computer vision and deep learning &mdash; segmentation, detection, classification, tracking. You understand the architectures well enough to make informed choices.</li>
<li>Fluency with modern ML approaches &mdash; VLMs, LLMs, foundation models, and agentic systems &mdash; alongside classical deep learning. You know when to fine-tune a ConvNet, when to prompt a VLM, and when to wire up an agent, and you understand the practical realities of putting any of them into a product.</li>
<li>Experience building ML training pipelines and data annotation systems at scale.</li>
<li>Experience evaluating ML models rigorously &mdash; designing metrics, building the eval harness, and using results to drive product decisions rather than just publish a number.</li>
<li>Proficiency with cloud ML infrastructure (AWS or equivalent) &mdash; you've managed training jobs, data pipelines, and experiment workflows in production.</li>
<li>Familiarity with cloud-to-edge model deployment.</li>
<li>Clear, direct communication &mdash; you can explain tradeoffs to non-technical stakeholders, push back honestly when you disagree, and write docs that others can follow.</li>
<li>Genuine interest in applying AI to food waste reduction and sustainability. This is a mission-driven product and we want people who care about the mission.</li>
</ul>
Software skills: Python, PyTorch, OpenCV. Strong familiarity with MLOps on AWS infrastructure. Experience with LLM and agent frameworks. Google Cloud / Gemini experience is a plus.
<h2 class="jobSectionHeader">
Nice to Have</h2>
<ul><li>Experience with video understanding (temporal consistency, tracking, video segmentation)</li>
<li>Experience with foundation models for data annotation</li>
<li>Experience with MLOps tooling (Weights &amp; Biases, MLflow, SageMaker, or equivalents)</li>
<li>Experience shipping LLM- or agent-powered features in a consumer or B2B product</li>
<li>Hardware / IoT product experience, particularly with computer vision and cameras for embedded systems</li>
</ul>
The estimated base salary range for this position is $240 to $280k, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs. At Mill, it is not typical for an individual to be hired at or near the top of the range for their role.</div>

AI Engineer, Computer Vision

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