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Deep Learning Researcher Jobs (NOW HIRING)

... deep learning framework (PyTorch, Tensorflow, Jax) $19 - $65 an hour Your opportunities joining PlusAI Work, learn and grow in a highly future-oriented, innovative and dynamic field. Wide range of ...

... deep learning framework (PyTorch, Tensorflow, Jax) $19 - $65 an hour Your opportunities joining PlusAI Work, learn and grow in a highly future-oriented, innovative and dynamic field. Wide range of ...

Senior Deep Learning Engineer

Santa Clara, CA · On-site

$65 - $83.75/hr

We collaborate extensively with diverse teams at NVIDIA, spanning deep learning research and framework development teams, to silicon architecture. Thriving in such a high-impact, interdisciplinary ...

Senior Deep Learning Engineer

Redmond, WA · On-site

$62 - $79.75/hr

We collaborate extensively with diverse teams at NVIDIA, spanning deep learning research and framework development teams, to silicon architecture. Thriving in such a high-impact, interdisciplinary ...

... Learning Researcher while also providing a truly unparalleled educational experience. You'll work ... Depending on the day, you might be diving deep into market data, tuning hyperparameters, debugging ...

IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a ...

Keep on top of the latest developments and research in academic CV/DL and decide how we should ... Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs ...

Deep Learning Engineer

Palo Alto, CA · On-site

$170K - $300K/yr

Keep on top of the latest developments and research in academic CV/DL and decide how we should ... Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs ...

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Deep Learning Researcher information

See salary details

$30K

$113.1K

$164.5K

How much do deep learning researcher jobs pay per year?

As of Jul 17, 2026, the average yearly pay for deep learning researcher in the United States is $113,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $154,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Deep Learning Researchers when transitioning models from research to production environments?

Deep Learning Researchers often encounter challenges such as ensuring model robustness, addressing scalability issues, and optimizing computational efficiency when moving models from research to production. Real-world data can be noisier or more variable than research datasets, requiring additional data preprocessing and validation. Collaboration with engineering teams is crucial to integrate models effectively, and researchers may need to adapt their algorithms to meet latency and resource constraints of production environments.

What is a Deep Learning Researcher?

A Deep Learning Researcher is a specialist who develops and investigates advanced algorithms and models that allow computers to learn from large amounts of data, particularly using artificial neural networks. They focus on designing, training, and evaluating deep learning models to solve complex problems in areas like computer vision, natural language processing, and speech recognition. Their work often involves staying up to date with the latest advancements in machine learning and contributing to research publications. Deep Learning Researchers may work in academia, industry, or research labs, collaborating with other scientists and engineers to push the field forward.

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

To thrive as a Deep Learning Researcher, you need a strong background in computer science, mathematics, and machine learning principles, typically supported by an advanced degree (Master’s or PhD) in a related field. Proficiency with frameworks like TensorFlow or PyTorch, experience with high-performance computing, and familiarity with relevant programming languages such as Python are essential. Strong problem-solving, collaboration, and communication skills help you innovate and effectively share research findings. These skills and qualities are crucial for advancing state-of-the-art models and driving impactful AI solutions.

What is the difference between Deep Learning Researcher vs Machine Learning Engineer?

AspectDeep Learning ResearcherMachine Learning Engineer
CredentialsAdvanced degrees in AI, CS, or related fields; research publicationsDegree in CS, Data Science, or related; practical experience
Work EnvironmentResearch labs, academia, R&D departmentsIndustry, product teams, deployment environments
Employer & Industry UsageUniversities, research institutes, tech companiesTech companies, startups, enterprise firms
Primary FocusDeveloping new algorithms, theoretical advancementsImplementing models, deploying solutions, optimizing systems

While both roles involve working with machine learning models, Deep Learning Researchers focus on advancing the theoretical foundations and developing new algorithms, often in research settings. Machine Learning Engineers apply these models in real-world applications, focusing on deployment and system optimization.

More about Deep Learning Researcher jobs
Infographic showing various Deep Learning Researcher job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $113,102 per year, or $54.4 per hour.
Molecular Dynamics | Machine Learning Researcher

Molecular Dynamics | Machine Learning Researcher

StaffRight Associates, LLC

Manhattan, NY • On-site

Other

Posted 3 days ago

New


Job description

Preface

Innovation in investment and technology research demands more than just data—it requires the seamless convergence of machine learning, computational chemistry, and biophysics and high-stakes execution. At the core of this firm, the challenge is not merely managing systems, but architecting the infrastructure that enables breakthroughs.

StaffRight Associates is sourcing for a world-class firm where computational excellence is the standard. We are seeking an elite professional to join the Machine Learning Research team. This is not a support role; it is a strategic position for a candidate who thrives in a research-intensive environment and understands that technical precision is the backbone of global-scale problem-solving.

Academic & Research Pedigree

Our client’s environment demands exceptional intellectual rigor. While academic achievements are highly valued, StaffRight Associates is specifically targeting elite candidates who possess advanced degrees (Master’s or Ph.D.) in quantitative or STEM-related disciplines. Additionally, a strong track record of published research, hands-on academic exploration, or innovative technical contributions in high-performance computing, quantitative finance, or advanced technology sectors is highly preferred.

The Mission

As a Molecular Dynamics – Machine Learning Researcher, you will act as a force multiplier for the team. Your mission is to eliminate systemic friction, allowing leadership to maintain an absolute focus on accelerating drug discovery, biomolecular simulation, and deep learning-driven molecular design.

You will transform abstract organizational challenges into high-impact, actionable outcomes. Working at the intersection of proprietary architecture and sophisticated algorithms, you will ensure that the firm’s technical output consistently pushes the boundaries of computational biochemistry and molecular biology.

Core Technical Objectives
  • Orchestrate Systemic Workflows: Own and execute complex, high-stakes protocols to ensure seamless operational velocity for the Machine Learning Research group.

  • Distill Complex Data: Spearhead technical research, translating multifaceted bio-computational and structural information into precise deep learning frameworks that drive strategic decisions.

  • Optimize Frameworks: Validate and refine intricate technical requirements, ensuring systemic accuracy, structural modeling fidelity, and operational resilience.

  • Engineer Multi-Project Solutions: Navigate concurrent projects using a proactive Goal-Execution-Mapping (GEM) approach to preempt bottlenecks across modeling, validation, and supercomputer deployment.

  • Facilitate Elite Communication: Serve as a key interface between the technical/scientific committee and critical stakeholders, upholding the highest standards of professional discretion.

Candidate DNA
  • Architectural Mindset: You approach complex deep learning and biophysical problems with a focus on scale, efficiency, and resilience, executing with autonomous precision under pressure.

  • Proactive Problem-Solving: You don''''''''t just react to issues; you anticipate systemic model bottlenecks (e.g., in protein folding, generative chemistry, or force field accuracy) and resolve them before they impact the research pipeline.

  • Communication Precision: You translate complex technical concepts—such as neural network architectures, quantum chemistry models, or molecular dynamics trajectories—into clear, articulate insights for elite scientific and business minds.

  • Cross-Functional Agility: You pivot effortlessly between rigorous, hands-on python development and deep scientific inquiry, maintaining momentum in a high-performance ecosystem.

The Opportunity

This is a unique opportunity to collaborate with world-class chemists, biologists, and computer scientists to expand the group’s efforts applying machine learning to drug discovery, biomolecular simulation, and biophysics.

What You Will Do
  • Develop advanced deep learning techniques applied to molecular systems.

  • Contribute to a rapidly growing ML research effort that has already successfully published neural networks improving quantum chemistry model accuracy and trained deep learning models to generate optimized molecules for drug discovery.

  • Work alongside specialized, proprietary technology—including multiple generations of the special-purpose supercomputer, which executes molecular dynamics simulations orders of magnitude faster than conventional systems.

What We Are Looking For
  • Strong Programming Skills: Outstanding Python programming abilities are a baseline requirement.

  • Domain Alignment: Experience in molecular dynamics, structural biology, medicinal chemistry, cheminformatics, and/or quantum chemistry is highly preferred.

  • Note: Specific knowledge of all these domains is less critical than intellectual curiosity, versatility, and a proven track record of achievement and innovation in the broader field of machine learning.

  • Collaborative Mindset: A desire to contribute to a stimulating, positive, and deeply collaborative research culture.

Partnering with StaffRight Associates

At StaffRight Associates, we operate at the nexus of technical alignment and structural execution. We do more than match resumes to keywords; we map your specific engineering DNA to the most demanding challenges in the industry.

When you partner with us, you are collaborating with an elite team that understands the nuances of high-stakes innovation. We are committed to placing talent where their contributions drive meaningful, systemic impact.