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Remote Phd Deep Learning Research Jobs (NOW HIRING)

Codertal is hiring a Deep Learning Engineer for a remote opportunity in the European Union on a B2B ... Research and experiment with the latest AI techniques and architectures (Transformers, CNNs, RNNs ...

... remote sensing applications. The role involves training, testing, deploying, and maintaining ... training deep learning models • Experience with transformer-based models, self-supervised ...

Deep "under-the-hood" understanding of modern neural network architectures and distributed training ... Remote-First Team - Work from anywhere in the U.S. * Unlimited PTO & 10 Holidays - So you can relax ...

Apply cutting-edge research in machine learning and computer graphics to solve real-world problems ... Interact with remote machines via a Unix shell to deploy and test code on large-scale geospatial ...

New

Senior Machine Learning Engineer

Boston, MA · On-site +1

$174K - $287K/yr

... productize deep learning research. If you are someone who wants to contribute to solving ... A PhD in a ML related domain is considered a strong plus. #LI-MD2 #AI-HIRING #vllm-1 The salary ...

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Remote Phd Deep Learning Research information

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$106K

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How much do remote phd deep learning research jobs pay per year?

As of Jun 6, 2026, the average yearly pay for remote phd deep learning research in the United States is $106,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $104,000.00 per year, depending on experience, location, and employer.

What is a Remote PhD Deep Learning Researcher?

A Remote PhD Deep Learning Researcher is a professional with a doctoral degree who conducts advanced research in deep learning, often from a location outside of a traditional office or laboratory. Their work involves developing and testing new algorithms, models, or techniques in areas such as computer vision, natural language processing, or reinforcement learning. They typically collaborate with academic, corporate, or independent research teams online, publish findings in scientific journals, and contribute to the advancement of artificial intelligence. Working remotely allows them to engage with global research communities and access a broader range of opportunities.

What are some common challenges faced by remote PhD Deep Learning researchers, and how can they be addressed?

Remote PhD Deep Learning researchers often encounter challenges such as limited in-person collaboration, access to specialized hardware, and balancing independent work with regular communication. Overcoming these obstacles typically involves leveraging collaborative tools (like Slack, Zoom, or GitHub), proactively scheduling virtual meetings with advisors and peers, and utilizing cloud computing resources for large-scale experiments. Building a strong online network and maintaining clear communication channels can greatly enhance productivity and foster a sense of community despite the remote setting.

What are the key skills and qualifications needed to thrive as a Remote PhD Deep Learning Researcher, and why are they important?

To thrive as a Remote PhD Deep Learning Researcher, you need a strong background in machine learning, deep learning theory, and advanced mathematics, typically backed by a PhD in computer science or a related field. Expertise with programming languages such as Python, deep learning frameworks like TensorFlow or PyTorch, and experience with cloud computing platforms is essential. Exceptional problem-solving, independent initiative, and clear scientific communication are crucial soft skills for remote collaboration and research dissemination. These skills ensure high-quality, innovative research and effective contributions to the scientific community in a remote setting.
Infographic showing various Remote Phd Deep Learning Research job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 57% Full Time, 39% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $106,012 per year, or $51 per hour.

Deep Learning Research Scientist

TalentPros.AI

San Francisco, CA • On-site, Remote

$250K - $510K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 12 days ago


Job description

Our mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.


At our firm, we believe the most impactful safety research will require access to frontier AI systems. The most powerful AIs will operate not just on text but also other modes of data, including images, video and audio. Such models have potential to augment human creativity and productivity in exciting ways. However, we are very concerned about the risks introduced by powerful multimodal AIs. The Multimodal team builds and studies multimodal models to better understand and mitigate these risks.

Our team works across many parts of a large stack that includes training, inference, system design and data collection. Some of our core focus areas are:


Foundational Research

We develop new architectures for modeling multimodal data and study how they interact with text-only models at scale.


Building Infrastructure

We work on many infrastructure projects including:

  • Complex multimodal reinforcement learning environments.
  • High-performance RPC servers for processing image inputs.
  • Sandboxing infrastructure for securely collecting data.


Data Ingestion

We are more interested in running simple experiments at large scale than smaller complex experiments. This requires access to very large sources of multimodal data. We develop tooling to collect, process and clean multimodal data at scale.

Because we focus on so many areas, the team is looking to work with both experienced engineers and strong researchers, and encourage anyone along the researcher/engineer curve to apply.


You may be a good fit if you:

  • Have significant software engineering experience
  • Are results-oriented, with a bias towards flexibility and impact
  • Pick up slack, even if it goes outside your job description
  • Enjoy pair programming (we love to pair!)
  • Want to learn more about machine learning research
  • Care about the societal impacts of your work


Strong candidates may also have experience with:

  • High performance, large-scale ML systems
  • GPUs, Kubernetes, PyTorch, or OS internals
  • Language modeling with transformers
  • Reinforcement learning
  • Large-scale ETL


The expected salary range for this position is:

Annual Salary: $230,000—$400,000 USD


Logistics

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.


Compensation and Benefits*

Our compensation package consists of three elements: salary, equity, and benefits. We are committed to pay fairness and aim for these three elements collectively to be highly competitive with market rates.

Equity - For eligible roles, equity will be a major component of the total compensation. We aim to offer higher-than-average equity compensation for a company of our size, and communicate equity amounts at the time of offer issuance.

US Benefits - The following benefits are for our US-based employees:

  • Optional equity donation matching.
  • Comprehensive health, dental, and vision insurance for you and all your dependents.
  • 401(k) plan with 4% matching.
  • 22 weeks of paid parental leave.
  • Unlimited PTO – most staff take between 4-6 weeks each year, sometimes more!
  • Stipends for education, home office improvements, commuting, and wellness.
  • Fertility benefits via Carrot.
  • Daily lunches and snacks in our office.
  • Relocation support for those moving to the Bay Area.



* This compensation and benefits information is based on our good faith estimate for this position as of the date of publication and may be modified in the future. Employees based outside of the US will receive a different benefits package. The level of pay within the range will depend on a variety of job-related factors, including where you place on our internal performance ladders, which is based on factors including past work experience, relevant education, and performance on our interviews or in a work trial.


How we're different

We believe that the highest-impact AI research will be big science. At our firm, we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.


The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to joining our firm, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.


Come work with us!

We are a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.