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Ml Research Jobs (NOW HIRING)

Jobgether

Our partner is looking for a ML Research Engineer / Scientist based in Netherlands. Join a research-driven team building next-generation AI models designed to understand complete CT studies rather ...

Analytical Mechanics Associates

AI/ML Research Associate

$87K - $97K/yr

We are seeking an AIML Research Associate to contribute to applied machine learning, data workflows ... Develop and evaluate ML models (e.g., regression/classification, time series, anomaly detection ...

New

SambaNova

ML Features Solutions Engineer

Austin, TX · On-site

Research to Product Bridge: Translate ML research innovations into practical product features and customer-facing capabilities. * Cross-Team Collaboration: Work closely with SDK, testing, and ...

Bostondynamics

Staff, ML Research Scientist

Waltham, MA

$154K - $192K/yr

As a Staff ML Research Scientist on the Machine Learning Safety R&D Team , you will join a small cross-functional group developing machine learning models that will enable our robots to operate ...

Boston Dynamics

Staff, ML Research Scientist

Waltham, MA · On-site

$154K - $192K/yr

As a Staff ML Research Scientist on the Machine Learning Safety R&D Team , you will join a small cross-functional group developing machine learning models that will enable our robots to operate ...

OpenAI

Recruiter, AI/ML Research

San Francisco, CA · On-site

$216K - $240K/yr

Proactively identify and cultivate exceptional AI/ML research talent across industry, academia, and emerging labs, often before formal hiring needs exist. * Use market insights and candidate signals ...

Modal, Inc

ML Research Intern

New York, NY · On-site

We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our ...

MIT Lincoln Laboratory

Design, program, and architect advanced ML methods. * Develop algorithms in speech, natural language processing, multimedia, cyber, and graph analytics. * Publish and present research at premier ...

Dynamo AI

ML Research Engineer -- LLM Safety

The ML Research Engineer will own an LLM vertical, generating synthetic data and pushing the boundaries of LLM safety techniques while collaborating with a team of researchers. Responsibilities : • ...

SambaNova

ML Features Solutions Engineer

Austin, TX · On-site

$200K - $270K/yr

Research to Product Bridge: Translate ML research innovations into practical product features and customer-facing capabilities. * Cross-Team Collaboration: Work closely with SDK, testing, and ...

Rad AI

Senior ML Research Scientist

San Francisco, CA · On-site

$170K - $220K/yr

Typically 4+ years of relevant applied ML research or engineering experience, or equivalent scope and impact. We calibrate on demonstrated ownership rather than title or exact tenure. * An MS, PhD ...

Elicit

ML Research Resident

Oakland, CA · On-site +1

$15K/mo

Elicit is building a research agent that can use an unlimited amount of test-time compute while ... But unlike typical ML systems that are often trained to do "whatever works", we need improvements ...

Exa

Research, ML

San Francisco, CA · On-site

$180K - $350K/yr

Research at Exa The ML organization sits at the heart of our mission. We train foundational models for search. Our goal is to build systems that can instantly filter the world's knowledge to exactly ...

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Ml Research information

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$108.7K

$185.5K

How much do ml research jobs pay per year?

As of Sep 1, 2026, the average yearly pay for ml research in the United States is $108,688.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,500.00 and $143,500.00 per year, depending on experience, location, and employer.

What is an ML researcher?

ML Researchers, or Machine Learning Researchers, are professionals who study and develop new algorithms and models in the field of machine learning. They work on advancing the theoretical foundations of machine learning and applying these concepts to solve real-world problems. Their work often involves designing experiments, analyzing data, publishing academic papers, and collaborating with other scientists and engineers. ML Researchers play a crucial role in pushing the boundaries of what artificial intelligence and machine learning can achieve.

What are some common challenges faced by professionals in ML research roles and how can they be addressed?

ML Research professionals often encounter challenges such as working with limited or noisy datasets, ensuring reproducibility of experiments, and keeping up with rapid advancements in the field. Collaboration with cross-functional teams, such as data engineers and domain experts, is essential to address data quality issues and validate results. Staying updated through research papers, conferences, and continuous learning helps tackle the evolving technical landscape. Building strong documentation and version control practices also ensures reproducibility and effective teamwork.

What are the key skills and qualifications needed to thrive as an ML researcher, and why are they important?

To thrive as an ML Researcher, you need strong foundations in mathematics, statistics, programming (often Python), and a graduate degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), version control systems, and experience with scientific publishing or conference presentations are typically required. Creativity, critical thinking, and effective communication are essential soft skills for generating novel ideas and collaborating within interdisciplinary teams. These skills and qualities are crucial for developing innovative solutions, advancing the field, and successfully translating research into impactful applications.

What is the difference between Ml Research vs Data Scientist?

AspectML ResearchData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fieldsBachelor's or Master's in CS, Statistics, or related fields
Work EnvironmentResearch labs, academia, R&D departmentsBusiness environments, analytics teams, product development
Employer & Industry UsageTech companies, research institutions, universitiesTech firms, finance, healthcare, e-commerce
Common Search & ComparisonFocuses on developing new algorithms and modelsFocuses on applying data analysis to solve business problems

ML Research and Data Scientist roles share overlapping skills but differ mainly in focus. ML Researchers primarily develop new algorithms and conduct theoretical work, often in research settings. Data Scientists apply existing models to analyze data and generate insights for business decisions. Both roles require strong technical skills, but ML Research emphasizes innovation and experimentation, while Data Science emphasizes practical application and communication of findings.

How to become a machine learning researcher?

To become a machine learning researcher, typically a strong background in computer science, mathematics, or related fields is required, often demonstrated through a relevant bachelor's degree followed by a master's or Ph.D. focusing on machine learning, artificial intelligence, or data science. Developing skills in programming languages like Python, understanding algorithms, and working with tools such as TensorFlow or PyTorch are essential. Gaining research experience through projects, publications, or internships can also improve prospects in this field.

Is ML research a high paying job?

ML research positions are generally well-paid, especially at senior levels or in industry-leading companies, due to the specialized skills and advanced knowledge required. Salaries can vary based on experience, education, location, and the complexity of projects, but they tend to be higher than average for tech roles.
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Cities with the most Ml Research job openings:

What are the most commonly searched types of Ml Research jobs?

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What states have the most Ml Research jobs?

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Ml Research jobs near you
Infographic showing various Ml Research job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $108,688 per year, or $52.3 per hour.

ML Research Engineer / Scientist

Jobgether

Remote

Full-time

Medical, Vision

This job post has expired today. Applications are no longer accepted.

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a ML Research Engineer / Scientist based in Netherlands.

Join a research-driven team building next-generation AI models designed to understand complete CT studies rather than isolated findings.
You'll work on foundation models, vision-language learning, and multi-finding detection using medical imaging data at unprecedented scale.
Your research will have a direct path from experimentation to regulatory submissions, hospital deployment, and real-world patient care.
You'll own models and experiments end to end, from developing the initial idea through training, evaluation, calibration, and production readiness.
You'll work alongside experienced ML engineers, software engineers, and fellowship-trained radiologists across multiple clinical specialties.
The role combines deep technical research with practical impact, giving you the opportunity to solve challenging problems in medical AI with exceptionally rich real-world data.
This is a fully remote opportunity for an independent researcher who wants their work to move quickly from the lab into clinical practice.

Accountabilities
  • Design, develop, train, and evaluate machine learning models capable of interpreting complete CT studies at the study level.
  • Research foundation-model approaches for medical imaging, including 3D and volumetric learning at large scale.
  • Develop and investigate vision-language models that connect medical images with the terminology and reporting patterns used by radiologists.
  • Build models capable of identifying and prioritizing multiple urgent clinical findings simultaneously while maintaining safe and clinically appropriate operating points.
  • Design and execute independent experiments, from hypothesis formation and architecture selection through training, evaluation, and analysis.
  • Develop custom architectures, training pipelines, loss functions, and distributed training approaches using modern deep learning frameworks.
  • Analyze model performance rigorously and establish reproducible evaluation methodologies suitable for clinically consequential AI systems.
  • Work closely with fellowship-trained radiologists to understand clinical requirements, interpret results, and translate research findings into practical model improvements.
  • Contribute to models and research that progress toward regulatory submissions, clinical deployment, and real-world patient use.
  • Take ownership of research projects end to end and make informed decisions about which experiments and approaches are most likely to deliver meaningful improvements.
  • Collaborate with ML and software engineering teams to move successful research from experimentation toward robust, deployable systems.
Requirements:
  • Strong practical experience with modern machine learning and deep learning, particularly using PyTorch for custom architectures, training loops, and experimentation.
  • Deep understanding of why machine learning architectures, objectives, optimization strategies, and training approaches work, rather than relying solely on existing implementations.
  • Demonstrated ability to independently formulate hypotheses, design experiments, interpret results, and iterate toward better models.
  • Strong understanding of rigorous experimentation, evaluation, reproducibility, and model validation.
  • Experience working with large-scale datasets and distributed training environments is highly valuable.
  • A strong interest in solving technically challenging problems where model performance and reliability have meaningful real-world consequences.
  • Ability to work effectively with researchers, engineers, and clinical experts in a collaborative environment.
  • Medical imaging, 3D computer vision, or volumetric-data experience is advantageous but not required.
  • Experience with vision-language models or self-supervised learning is a plus.
  • Familiarity with DICOM, CT imaging, radiology, or other medical-data formats and workflows is beneficial.
  • A PhD, research publications, or a strong academic research background is a plus, but not a prerequisite.
  • Prior medical-AI experience is not required; a willingness to learn clinical concepts directly from radiology experts is valued.
  • Strong written and verbal communication skills and the ability to work independently in a fully remote environment.
Benefits:
  • Fully remote position open to candidates worldwide.
  • Location-flexible compensation with a cash-weighted base salary determined according to the local market in the country where you work.
  • Specific compensation range for your location shared early in the hiring process.
  • No equity included in international offers, with compensation structured transparently around local-market cash pay.
  • Opportunity to work with a real-world CT dataset covering approximately 10 million patients, paired with radiology reports.
  • Direct collaboration with fellowship-trained radiologists across areas including chest, body, MSK, neuro, and oncology.
  • Opportunity to work on research that can progress from experimentation to FDA submissions, hospital deployments, and patient care within months.
  • Exposure to large-scale foundation models, vision-language learning, distributed training, medical imaging, and clinically focused AI evaluation.
  • High degree of ownership over research ideas, experiments, models, and technical direction.
  • Opportunity to work alongside researchers and engineers with significant contributions to medical AI, open datasets, algorithms, and clinical AI systems.
  • A small, research-oriented team where successful ideas can move quickly from research into production.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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