Deep Learning

Deep Learning Engineer Jobs

189 jobs found

Today

Machine Learning Engineer, Digital Experience

Everpure Santa Clara, CA

Santa Clara, CA

Today

$180k - $270k/yr Full time Artificial Intelligence Data & Ai Data Analysis

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Job Description

Everpure is looking for a Machine Learning Engineer to help the Digital Experience Insights team move models from prototype to production. This role combines model development with production engineering, including pipelines, infrastructure, and the practices that keep deployed models performing reliably over time. You will collaborate across Data Science, Data Engineering, and Software Engineering to translate business questions into working systems that can be monitored, validated, and maintained.

What you’ll do

Move machine learning models from prototype to production by building dependable, scalable pipelines for training, serving, and inference.
Design and maintain data pipelines, feature stores, and workflow orchestration so models receive clean, timely, and well-tested inputs.
Implement monitoring for model performance, data drift, and pipeline health, and take action when issues arise.
Build and validate machine learning models and statistical approaches when needed, partnering with the broader data science team on model design.
Work with Data Scientists, Data Engineers, and Software Engineers to turn open-ended business questions into a clear technical plan and a production-ready system.
Work primarily from the Santa Clara office in accordance with Everpure’s policies, unless on PTO, work travel, or other approved leave.

What you bring

Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Engineering, Statistics, or a related field, or equivalent practical experience.
3 to 5 years of industry experience in data engineering, ML engineering, or a hybrid data science/engineering role, with a track record of shipping models to production.
Strong software engineering foundations in Python and SQL, including the ability to write production-quality, well-tested code.
Hands-on experience with a workflow orchestration tool such as Airflow or Dagster.
Experience in a cloud-native environment such as AWS, GCP, or Azure.
Working knowledge of machine learning and statistical modeling, with familiarity such as Scikit-Learn or PyTorch, plus enough grounding to build or extend a model when needed.
Good communication skills, including the ability to explain technical work clearly to non-technical stakeholders.
Comfort working through ambiguity and shifting priorities, with a collaborative approach across teams.

Tools you’ll use

Python, SQL, Airflow, Dagster, AWS, GCP, Azure, Scikit-Learn, PyTorch

What you can expect from Everpure

Innovation: Critical thinking, a love of challenges, and ambition to lead.
Growth: Support and space to grow while contributing to something meaningful.
Team: Mutual support and setting aside ego for the greater good.
Perks for balance: Flexible time off, wellness resources, and company-sponsored team events.
Accessibility: Candidates with disabilities may request accommodations for the hiring process by contacting TA-Ops@purestorage.com.
Inclusive culture: Everpure is proud to be an equal opportunity employer where every voice matters.

Location: Santa Clara, CA (onsite)
Salary: USD 180,000 - 270,000 per year
Experience: 3+ years

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Today

Staff Machine Learning Engineer

Unity Technologies SF Mountain View, CA

Mountain View, CA

Today

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

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Job Description

Build production-grade AI for game experiences at Unity Technologies SF. This onsite role in Mountain View focuses on taking cutting-edge computer vision and multi-modal research into dependable, scalable systems across cloud, server, and on-device targets, with a team culture centered on measurement, engineering rigor, and cross-functional collaboration.

Responsibilities

Help set the technical vision and roadmap for computer vision and multi-modal AI models, including 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, plus multi-modal reasoning over images, text, and 3D inputs.
Make architecture and delivery decisions across quality, capability, latency, and cost for deployment targets, including training strategy, data pipelines, and evaluation.
Own the path from research prototype to production: training, fine-tuning, distillation, export, and serving, spanning cloud GPUs to efficient on-device inference where needed.
Collaborate closely with research scientists to translate novel CV and multi-modal architectures into deployable, well-engineered implementations.
Build scalable multi-modal inference systems that handle diverse inputs (images, video, text, primitives, and metadata) and generate outputs from semantic predictions to pixel-level generation.
Rapidly adopt field breakthroughs in areas such as vision-language pretraining and alignment, efficient diffusion (consistency models, flow matching), efficient attention (including FlashAttention and linear-attention variants), and tokenization or representation learning for vision.
When latency or device constraints require it, apply compression approaches such as quantization, pruning, and knowledge distillation, and work with runtimes including TensorRT, ONNX Runtime, CoreML, and TFLite.
Lead and mentor ML engineers, define engineering best practices and code review standards, and establish rigorous benchmarking and evaluation methodology.
Partner with research, platform engineers, product managers, and runtime teams to align ML capabilities with product roadmaps and target-platform constraints.
Champion a measurement culture by defining KPIs for model quality, accuracy, latency, memory, and cost, and ensuring consistent tracking.

Requirements

6+ years of ML engineering experience with substantial depth in computer vision and/or multi-modal modeling.
Production experience with transformer-based and diffusion-based vision models, including examples such as ViT, CLIP/SigLIP-style encoders, Stable Diffusion, and DETR/SAM-style architectures.
Strong command of the full model lifecycle, 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 techniques.
Strong Python skills and modern deep-learning tooling (notably PyTorch), plus solid software engineering fundamentals.
Proven technical leadership, including 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 172,200 - 283,900 per year
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 (such as annual discretionary bonuses or sales commissions)
Final offer amount 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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Today

Senior Machine Learning Engineer

Adobe San Jose, CA

San Jose, CA

Today

$152k - $265k/yr Full time AI Application Security Artificial Intelligence

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Job Description

As part of Adobe’s Search, Discovery & Content AI (SDC) team, you will help design and deliver large-scale machine learning systems that support generative AI and content understanding and discovery used by millions of people. This is a hands-on role spanning end-to-end system design through deployment and ongoing optimization, with opportunities to build GPU-optimized modeling and ML pipeline platform capabilities, while working closely with architects, product, and engineering teams.

What you’ll do

Define and develop end-to-end machine learning systems using innovative architectures
Develop and implement scalable, GPU-optimized modeling algorithms that can operate on large-scale data in production environments
Build and maintain high-performance, scalable, maintainable platform features across the complete ML pipeline
Partner with architects, product management, and engineering teams to translate product requirements into technical solutions
Design and develop runtimes and libraries for emerging ML technologies to help keep the team at the forefront of the field
Contribute to creation of detailed requirements and design documents for features across the technology stack

What you bring

PhD or master’s degree in computer engineering, Computer Science, Computer Vision, Robotics, or a related field (or equivalent experience)
Deep understanding of generative AI, deep learning, computer vision, and recommendation systems, with a track record applying these methods to complex problems
Proficiency with ML frameworks and tools such as Scikit-learn, Hugging Face, PyTorch, and PyTorch Lightning
Expertise with cloud technologies, Docker, and containerization
Experience with AWS, Microsoft Azure, or equivalent cloud platforms
Proficiency in one or more programming languages including Python, C++, Java, and Rust
Exceptional analytical and quantitative problem-solving skills
Excellent communication and relationship-building skills
Demonstrated ability to thrive in a collaborative team environment

Tools and technologies

Scikit-learn, Hugging Face, PyTorch, PyTorch Lightning
Docker, AWS, Microsoft Azure
Python, C++, Java, Rust

Interview AI use notice

The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.

Pay range

U.S.: $151,800 - $265,350 annually.

California: $183,300 - $265,350 annually.

Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP). In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

State-specific notices

California: Fair Chance Ordinances. Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.
Colorado: Application Window Notice. If this role is open to hiring in Colorado, the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If Colorado is not listed, no specific application window applies and the posting may close based on hiring needs.
Massachusetts: Massachusetts Legal Notice. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment.

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Today

AI/Machine Learning Engineer SME

Integration Innovation, Inc. (i3) Washington, DC

Washington, DC

Today

$200k - $350k/yr Full time Artificial Intelligence Data Architecture Data Engineer

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Job Description

SME-level AI/Machine Learning Engineer to architect enterprise-ready AI/ML solutions for the Defense Intelligence Agency (DIA) Enterprise Data Management & Analytics (EDMA) effort.

Responsibilities

Architect enterprise-ready AI/ML solutions, covering model-training pipelines, inference services, feature stores, data-ingestion pathways, and MLOps frameworks aligned to requirements
Define the technical roadmap by translating complex mission needs into actionable architectures, workstreams, and implementation plans
Lead multi-disciplinary engineering teams, directing algorithms, model selection, data strategy, integration, and delivery cadence
Oversee sprint planning and technical execution to keep deliverables on schedule, clear blockers, and ensure engineering artifacts meet enterprise standards
Act as the primary technical interface with government stakeholders, explaining architecture decisions, model behaviors, risk tradeoffs, and recommended solution pathways
Establish and enforce best practices across ML engineering, DevSecOps, coding standards, test automation, and model life-cycle governance
Run technical reviews and mentor engineers to raise team capability and maintain alignment to mission outcomes
Guide evaluation and adoption of advanced ML tools and approaches, including PyTorch, TensorFlow, JAX, distributed compute, MLOps platforms, generative-AI modernization patterns, and enterprise-scale data solutions
Integrate AI capabilities into cloud-native or hybrid architectures while meeting security, accreditation, and enterprise data management requirements
Provide SME support for strategic planning, including solution briefs, architecture diagrams, CONOPS development, and decision-ready artifacts for government review panels
Champion responsible-AI and governance aligned to IC and DoD directives

Requirements

U.S. Citizenship
Active Secret clearance with the ability to obtain and maintain TS/SCI
Bachelor’s degree (Master’s preferred) in Computer Science, Engineering, Applied Mathematics, Data Science, or a related field
Minimum 14 years of progressive experience in AI/ML, data engineering, or advanced analytics
Demonstrated experience architecting and deploying enterprise AI/ML systems
Deep proficiency in Python and modern ML frameworks, including PyTorch, TensorFlow, and JAX
Experience leading technical teams, performing architecture trade studies, and guiding development across the full ML life cycle
Strong understanding of secure software engineering, distributed systems, DevSecOps, and containerization
Excellent communication skills and ability to interface with senior government stakeholders

Technologies

Python
PyTorch
TensorFlow
JAX
MLOps
DevSecOps

Preferred Qualifications

Experience supporting AI/ML projects in government, defense, or intelligence environments
Familiarity with Intelligence Community mission needs and data types
Exposure to cloud-native development (AWS, Azure, GovCloud)
Experience building or supporting NLP, computer-vision, generative-AI, or anomaly-detection models
Knowledge of MLOps, CI/CD, container orchestration, or distributed training frameworks

Location: Washington, DC (hybrid)

Salary: USD 200,000 - 350,000 per year

Company: Integration Innovation, Inc. (i3)

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1d ago

Senior Machine Learning Engineer

Intuit Mountain View, CA

Mountain View, CA

1d ago

$171k - $232k/yr Full time Ai Engineer Artificial Intelligence Automation

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Job Description

Join Intuit in Mountain View, CA (onsite) and help bring machine learning from prototype to dependable, scalable production. This role focuses on architecting, coding, optimizing, and deploying ML models at scale, with an emphasis on automating delivery, monitoring outcomes, and continuously improving performance for customer impact.

Compensation: USD 171,000 - 231,500 per year.

What you’ll do

Architect and build ML systems that improve scalability, usability, and performance.
Collaborate cross functionally with product managers, data scientists, and engineers to understand needs, implement solutions, refine approaches, and design machine learning and related algorithms.
Communicate results effectively to peers and leaders.
Evaluate state-of-the-art technologies and apply them to deliver customer benefits.
Work with a variety of data sources, partnering with others to refine features and build end-to-end pipelines.

How the work shows up

Model productionalization: partner with data scientists to move prototype models toward customer-scale use. This can include increasing training data, automating training and prediction, and orchestrating pipelines for continuous prediction.
Model comparison and metrics: apply understanding of the underlying data details and provide metrics to compare models.
Model enhancement: improve existing codebases to raise prediction performance or reduce training time, including both exploratory work and directed improvements based on performance needs and ideas from data science teammates.
Machine learning tools: build project-focused tools that reduce friction in the data science process, such as speeding up training, making data processing easier, or improving data management. These tools may span multiple projects and are generally decoupled from any single initiative.

Key skills and focus areas

You’ll be expected to help architect, code, optimize, and deploy ML models at scale using current industry tools and techniques. The role also includes automating, delivering, monitoring, and improving machine learning solutions, with strong emphasis on software development, systems engineering, data wrangling, feature engineering, architecting, and testing.

Requirements

Education: BS, MS, or PhD degree in Computer Science or related field (or equivalent practical experience).

Technologies you’ll work with

Programming: Scala, Java, Python, SQL
ML/DL: SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras
Big data: Spark, Hive, Flink
Cloud/ML ops: AWS, AWS Sagemaker, Docker, Kubernetes

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1d ago

Machine Learning Engineer, Search and Shopping

Google Mountain View, CA

Mountain View, CA

1d ago

$207k - $300k/yr Full time Ad Tech Agentic Ai Ai Agent

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Job Description

Build and scale low-latency machine learning for Search and Shopping Ads, optimizing predicted click-through rate (pCTR) models and ad integration in emerging AI search experiences.

Responsibilities

Own technical architecture, delivery, and cross-team strategy for Search and Shopping Ads pCTR models in close partnership with DeepMind, Research, and Ads Machine Learning teams.
Design, prototype, and scale high-capacity pCTR architectures that maximize Tensor Processing Unit (TPU) performance while meeting strict low-latency serving and return-on-investment budgets.
Develop modeling solutions that capture deep user history and nuanced attention signals, integrating ads into emerging AI Search experiences including AI Overviews and AI Mode.
Engineer mathematical loss functions and calibration methods to translate business objectives into improvements across top-line metrics and auction outcomes.
Build agentic machine learning workflows to automate and accelerate optimal model architecture and feature space discovery.

Requirements

Bachelor’s degree or equivalent practical experience.
8 years of experience with software development, including 5 years with large-scale machine learning, deep learning, neural networks, or recommendation systems.
Experience designing and implementing large-scale production deep learning or neural network architectures under latency and computational constraints.
Experience leading cross-functional technical projects and mentoring other engineers.

Preferred Qualifications

PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Experience with agent-driven ML exploration, hyperparameter tuning, or automated model architecture search.
Experience with one or more of the following:
- Loss engineering for business objectives
- Joint modeling across distinct prediction stacks
- Hardware-aware ML optimizations (example: leveraging dense compute/TPUs effectively)
Familiarity with ads prediction systems, auction dynamics, or serving infrastructure (example: AdBrain, Admixer).
Ability to collaborate with peer technical leads and advanced ML research organizations (such as DeepMind or Google Research) to translate academic or exploratory methods into production systems.

Technologies

Tensor Processing Unit (TPU)
Large-scale machine learning
Deep learning
Neural networks
Recommendation systems
Sequence modeling
Agentic artificial intelligence workflows
Agentic machine learning workflows
Loss functions
Calibration methods

Compensation and Location

Location: Mountain View, CA (onsite)
Salary: USD 207,000 - 300,000 per year
US compensation package: $207,000 - $300,000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.

About the Job

Invent novel low-latency architectures that evaluate layouts in milliseconds while maximizing TPU capabilities.
With DeepMind and Research, design sequence modeling to capture deep user history across AI experiences including Artificial Intelligence Overviews and Artificial Intelligence Mode.
Engineer loss functions for auction dynamics and deploy agentic artificial intelligence workflows to accelerate model discovery.

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1d ago

Staff Machine Learning Engineer - Generative AI

Xometry Denver, CO

Denver, CO

1d ago

$140k - $230k/yr Full time AI Ai Ml Artificial Intelligence

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Job Description

Xometry is looking for a Staff Machine Learning Engineer to lead its Generative AI efforts focused on manufacturing-related challenges. This hybrid role is based in Denver, CO and centers on building and deploying multimodal generative models that help interpret documents and extract structured data.

You will collaborate across engineering and business teams to translate AI research and production-ready engineering into real product experiences across Xometry’s marketplace, using cloud infrastructure and modern ML tooling to operate at scale.

What you’ll do

Set the technical direction for the Generative AI team by defining best practices and driving high-impact innovation
Shape the AI roadmap by identifying where generative AI can create the most value across Xometry’s marketplace
Design, develop, and deploy large language and generative models for multimodal document processing and structured data extraction
Experiment with ways to combine text, images, and 3D data to improve solution quality and speed
Build data pipelines and training workflows capable of handling large, complex datasets
Use AWS and other platforms to train, optimize, and deploy models into production at scale
Partner with engineers, product leaders, and business teams to integrate AI capabilities into real products and customer workflows
Mentor teammates on advanced ML methods, model architecture, and best practices to raise team capability
Stay current with generative AI and deep learning research and bring new ideas into production

What you’ll need

Bachelor’s degree required; an advanced degree (M.S. or PhD) in Computer Science, Machine Learning, AI, or a related field is a plus
5+ years of experience in machine learning or data science, with deep expertise in generative models, LLMs, or computer vision
Demonstrated work building large-scale language and vision models (Transformers, GPT, VLMs)
Hands-on experience with multimodal data including text, images, and 3D
Proficiency in Python and key ML libraries: PyTorch, TensorFlow, pandas, and NumPy
Strong foundation in probability, statistics, and optimization for generative modeling
Experience deploying ML/AI models using cloud microservice architecture, with AWS preferred
Strong software engineering skills, including object oriented programming, testing, version control, CI/CD best practices, and IaC (terraform preferred)
Proven ability to communicate effectively with stakeholders across the organization, from executives to product managers
Background in manufacturing or supply chain is a plus; curiosity and drive are emphasized
Must be a U.S. Citizen or Green Card holder for ITAR compliance

Technologies

AWS
Python
PyTorch
TensorFlow
pandas
NumPy
Transformers
GPT
VLMs
CI/CD
IaC
terraform

Compensation and location

Location: Denver, CO (hybrid)
Salary: USD 140,000 - 230,000 per year
Experience: Minimum 5 years

Benefits

401(k) match
Medical, dental and vision insurance
Life and disability insurance
Generous paid time off including vacation, sick leave, floating and fixed holidays
Maternity and bonding leave
EAP
Other wellbeing resources