Deep Learning Engineer Jobs
Deep Learning Engineer jobs are open across technology, healthcare, automotive, finance, and research institutions, from new-grad to staff and principal levels, with specializations in computer vision, natural language processing, and generative AI. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 56+ Deep Learning Engineer jobs
Company:
Qualcomm Technologies, Inc.Job Area:
Engineering Group, Engineering Group > Machine Learning ResearcherGeneral Summary:
Qualcomm AI Research is seeking world-class researchers to help push the boundaries of artificial intelligence and machine learning. Our team pioneers new algorithms, models, and systems that advance the state of the art while delivering exceptional performance with unprecedented power, memory, and computational efficiency.
Our multidisciplinary team brings together expertise across algorithms, software, hardware, and systems. We pursue fundamental and applied research, from foundational theories and novel learning algorithms to efficient AI systems deployed on resource-constrained devices. Working closely with Qualcomm's business and engineering teams, we translate research breakthroughs into technologies deployed worldwide across mobile, compute, automotive, robotics, cloud, XR, and IoT platforms, while publishing at leading AI conferences.
Our team has demonstrated world-first research breakthroughs and proof-of-concepts across model efficiency, representation learning, perception and understanding, generative and foundation models, reasoning and agentic intelligence, and embodied and physical AI. Our research contributions have earned numerous best paper awards, major research competition wins, and top rankings on AI and computer vision benchmarks. We also translate these breakthroughs into highly optimized AI models, end-to-end workflows, and developer tools for Qualcomm platforms, making many innovations accessible to all. Explore our research and innovations:
- Research Breakthroughs and Demonstrations
- Qualcomm AI Research Publications and Innovations Qualcomm AI Hub: Optimized Models and Workflows
Role Responsibilities:
Research interns will contribute to fundamental and applied research in artificial intelligence and machine learning, developing novel algorithms, models, and systems that advance the state of the art. Research opportunities span a broad range of areas, including:
- Generative AI and Foundation Models: Large language models (LLMs), multimodal foundation models, vision-language models (VLMs), vision-language-action models (VLAs), world models, and generative modeling for language, image, video, audio, and 3D content. Research topics include diffusion and autoregressive models, mixture-of-experts, parallel decoding, long-context modeling, and knowledge distillation.
- Agentic AI and Reasoning: Autonomous agents, multi-agent systems, reasoning, planning, and verification; memory management, retrieval-augmented generation (RAG), inference-time optimization, robustness, alignment, AI safety, and hallucination mitigation.
- Efficient and Scalable AI: Novel neural architectures, efficient attention mechanisms, transformers, state-space models, and recurrent networks; model compression, pruning, quantization, neural architecture search, parameter-efficient fine-tuning, and efficient training and inference. Research also includes on-device AI, edge-cloud learning, distributed training, hardware-aware optimization, and compiler and kernel optimizations.
- Learning, Perception, and Embodied AI: Self-supervised, reinforcement, imitation, contrastive, and continual learning; few-shot learning, personalization, domain adaptation, and uncertainty estimation. Applications include computer vision, 3D perception, neural rendering, speech AI, autonomous driving, robotics, embodied intelligence, and human-AI interfaces.
Depending on the research project, internship responsibilities may include:
- Conducting original research, developing novel machine learning algorithms and architectures, and designing, training, and evaluating AI models to advance the state of the art.
- Developing efficient AI systems and software frameworks, including optimization for mobile, edge, and cloud platforms.
- Exploring emerging computing paradigms, including hardware-software co-design, compute-in-memory, and distributed or federated learning.
- Collaborating with researchers and engineers to prototype, validate, and communicate research findings, with opportunities to contribute to scientific publications and technology transfer.
Minimum Qualifications:
- Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- 6+ months of experience developing and/or optimizing machine learning models, systems, platforms, or methods.
Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
To all Staffing and Recruiting Agencies : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
Pay range and Other Compensation & Benefits :
$178,200.00 - $267,400.00The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link .
If you would like more information about this role, please contact Qualcomm Careers .
Company:
Qualcomm Technologies, Inc.Job Area:
Engineering Group, Engineering Group > Machine Learning ResearcherGeneral Summary:
Qualcomm AI Research is seeking world-class researchers to help push the boundaries of artificial intelligence and machine learning. Our team pioneers new algorithms, models, and systems that advance the state of the art while delivering exceptional performance with unprecedented power, memory, and computational efficiency.
Our multidisciplinary team brings together expertise across algorithms, software, hardware, and systems. We pursue fundamental and applied research, from foundational theories and novel learning algorithms to efficient AI systems deployed on resource-constrained devices. Working closely with Qualcomm's business and engineering teams, we translate research breakthroughs into technologies deployed worldwide across mobile, compute, automotive, robotics, cloud, XR, and IoT platforms, while publishing at leading AI conferences.
Our team has demonstrated world-first research breakthroughs and proof-of-concepts across model efficiency, representation learning, perception and understanding, generative and foundation models, reasoning and agentic intelligence, and embodied and physical AI. Our research contributions have earned numerous best paper awards, major research competition wins, and top rankings on AI and computer vision benchmarks. We also translate these breakthroughs into highly optimized AI models, end-to-end workflows, and developer tools for Qualcomm platforms, making many innovations accessible to all. Explore our research and innovations:
- Research Breakthroughs and Demonstrations
- Qualcomm AI Research Publications and Innovations Qualcomm AI Hub: Optimized Models and Workflows
Role Responsibilities:
Research interns will contribute to fundamental and applied research in artificial intelligence and machine learning, developing novel algorithms, models, and systems that advance the state of the art. Research opportunities span a broad range of areas, including:
- Generative AI and Foundation Models: Large language models (LLMs), multimodal foundation models, vision-language models (VLMs), vision-language-action models (VLAs), world models, and generative modeling for language, image, video, audio, and 3D content. Research topics include diffusion and autoregressive models, mixture-of-experts, parallel decoding, long-context modeling, and knowledge distillation.
- Agentic AI and Reasoning: Autonomous agents, multi-agent systems, reasoning, planning, and verification; memory management, retrieval-augmented generation (RAG), inference-time optimization, robustness, alignment, AI safety, and hallucination mitigation.
- Efficient and Scalable AI: Novel neural architectures, efficient attention mechanisms, transformers, state-space models, and recurrent networks; model compression, pruning, quantization, neural architecture search, parameter-efficient fine-tuning, and efficient training and inference. Research also includes on-device AI, edge-cloud learning, distributed training, hardware-aware optimization, and compiler and kernel optimizations.
- Learning, Perception, and Embodied AI: Self-supervised, reinforcement, imitation, contrastive, and continual learning; few-shot learning, personalization, domain adaptation, and uncertainty estimation. Applications include computer vision, 3D perception, neural rendering, speech AI, autonomous driving, robotics, embodied intelligence, and human-AI interfaces.
Depending on the research project, internship responsibilities may include:
- Conducting original research, developing novel machine learning algorithms and architectures, and designing, training, and evaluating AI models to advance the state of the art.
- Developing efficient AI systems and software frameworks, including optimization for mobile, edge, and cloud platforms.
- Exploring emerging computing paradigms, including hardware-software co-design, compute-in-memory, and distributed or federated learning.
- Collaborating with researchers and engineers to prototype, validate, and communicate research findings, with opportunities to contribute to scientific publications and technology transfer.
Minimum Qualifications:
- Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- 6+ months of experience developing and/or optimizing machine learning models, systems, platforms, or methods.
Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
To all Staffing and Recruiting Agencies : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
Pay range and Other Compensation & Benefits :
$178,200.00 - $267,400.00The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link .
If you would like more information about this role, please contact Qualcomm Careers .
Deep Learning Engineer Jobs by Experience Level
See All 56 Deep Learning Engineer Jobs
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Find Deep Learning Engineer JobsDeep Learning Engineer Job Market
Who's Hiring
- NVIDIA24
- Bot Auto4
- DoorDash2
- Quidient2
- Hayden AI2
Top Industries Hiring
- Technology & Software20
- Manufacturing2
- Science & Research2
- Artificial Intelligence2
- Aerospace & Defense1
What Employers Look For
The qualifications that appear most often in deep learning engineer jobs.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience designing and training neural networks for production or research applications
- Familiarity with model optimization techniques including quantization, pruning, and distillation
- Bachelor's or master's degree in computer science, electrical engineering, or a related field
- Hands-on experience with GPU computing and distributed training infrastructure
- Knowledge of MLOps practices including model versioning, monitoring, and deployment pipelines
Tips for Your Deep Learning Engineer Job Search
Tailor your resume to model types
Hiring managers scan for the specific architectures you've worked with, not just 'deep learning' as a category. Call out whether your experience is in transformers, diffusion models, CNNs, or RNNs so your resume matches the exact stack a team is running.
Publish reproducible project code publicly
A public repository with clean, documented training pipelines does more than a bullet point claiming model accuracy. Deep learning roles almost always involve a technical screen, and interviewers frequently pull up your code before the call even starts.
Filter openings by compute environment
Job listings that mention specific hardware, like on-premise GPU clusters versus cloud TPUs, signal very different day-to-day workflows. Match your search to the infrastructure you've actually trained on so you're not underselling or misrepresenting your background.
Apply early to roles that fit
Migrate Mate lists deep learning engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a benchmark story for interviews
Interviewers for these roles ask how you measured success on a model, not just whether it worked. Have one concrete example ready that explains your evaluation metric choice, your baseline, and what your final result improved over it.
Negotiate around compute access, not just salary
When you receive an offer, ask what GPU budget or cloud credits are available for experimentation. Teams that underfund compute often bottleneck their engineers, and that detail tells you more about the role's real scope than the job title does.
Deep Learning Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most deep learning engineers?
The companies hiring the most deep learning engineers right now include NVIDIA, Bot Auto, and DoorDash, with the largest share of openings in California, Texas, and Massachusetts, based on current listings on Migrate Mate as of November 2026. Demand is especially concentrated at organizations running large-scale model training or deploying AI into consumer and enterprise products.
How many deep learning engineer jobs are remote?
About 27% of deep learning engineer openings are fully remote or hybrid as of November 2026, reflecting strong employer flexibility for roles that don't require on-site hardware access. Research-oriented positions and those focused on NLP or generative AI tend to have the highest remote availability, while computer vision roles tied to physical hardware or robotics are more often on-site.
How do you become a deep learning engineer?
Start by building a strong foundation in linear algebra, calculus, and probability, then work through the core architectures: feedforward networks, CNNs, RNNs, and transformers. Implement models from scratch before relying on frameworks so you understand what the library is doing. Build a portfolio of end-to-end projects with public code, contribute to open-source repositories, and apply to roles where your stack overlaps with the team's existing infrastructure.
Can you get hired as a deep learning engineer without much experience?
Yes, but your portfolio has to do the work your resume can't. Employers hiring early-career candidates look for demonstrated ability to train and evaluate models, not just coursework. A well-documented project that solves a real problem, shows your data pipeline, reports honest benchmark results, and includes a clear write-up of what didn't work will outperform a credential-heavy resume with no shipped output.
What does the deep learning engineer interview process look like?
Most processes run three to four stages. A recruiter screen is followed by a technical phone interview covering ML fundamentals, coding, and sometimes a take-home problem. The on-site or virtual loop typically includes a system design round focused on ML pipelines, a coding round, and a research or project deep-dive where you walk through past work in detail. Some teams also include a presentation round for more senior roles.
Where can I find and apply to deep learning engineer jobs?
You can find and apply to deep learning engineer jobs on Migrate Mate, which lists current openings from employers across the United States. Search the listings to find roles that match your background and specialization, then apply directly to each one that fits.
See All 56 Deep Learning Engineer Jobs
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