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Full Time Machine Learning Researcher Jobs (NOW HIRING)

Inference

Machine Learning Researcher

San Francisco, CA · On-site

$250K - $350K/yr

Perform reinforcement learning research to improve model alignment and capability * Develop and improve our distillation pipeline for training high-quality models from frontier teachers * Train ...

Apple

AIML - Machine Learning Researcher, MLR

Cupertino, CA · On-site

$181K - $318K/yr

Play a part in building the next revolution in machine learning technology. We're looking for passionate researchers to work on ambitious, curiosity-driven, long-term research projects. In this role ...

Strivector Corp

Machine Learning

Manhattan, NY · On-site

They are seeking a Machine Learning professional capable of tackling research problems with commercial applications, applying technical expertise to real-world financial and operational challenges.

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Full Time Machine Learning Researcher information

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$30K

$113.1K

$164.5K

How much do full time machine learning researcher jobs pay per year?

As of Jun 19, 2026, the average yearly pay for full time machine 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.

Which 5 jobs will survive AI?

Full Time Machine Learning Researchers are likely to continue to find roles in developing and improving AI systems, as their expertise in designing algorithms and models remains essential. Jobs that require complex problem-solving, creativity, and human judgment, such as healthcare professionals, educators, skilled tradespeople, and certain managerial roles, are also expected to persist despite AI advancements. These roles often involve tasks that are difficult for AI to fully replicate or replace.

What is the difference between Full Time Machine Learning Researcher vs Data Scientist?

AspectFull Time Machine Learning ResearcherData Scientist
CredentialsAdvanced degrees in CS, ML, or related fieldsDegree in CS, statistics, or related fields; often includes certifications
Work EnvironmentResearch labs, academic institutions, R&D departmentsBusiness environments, tech companies, analytics teams
Industry UsagePrimarily in research-focused roles, developing new algorithmsApplying models to solve business problems, data analysis

While both roles require strong technical skills and knowledge of machine learning, Full Time Machine Learning Researchers focus on developing new algorithms and advancing ML theory, often in research settings. Data Scientists apply existing models to analyze data and generate insights for business decisions. The roles overlap in skills but differ in focus and work environment.

Which 3 jobs will survive AI?

Full Time Machine Learning Researchers are likely to continue to find roles in developing and improving AI systems, as their expertise in designing algorithms and models is essential. Other jobs expected to survive AI include healthcare professionals like doctors and nurses, who require human judgment and empathy, and skilled trades such as electricians and plumbers, which involve hands-on work difficult to automate fully.

Is ML research oversaturated?

Full-time machine learning research positions are competitive due to high demand for AI expertise, but opportunities continue to grow as industries adopt AI solutions. Success often depends on strong technical skills, research experience, and staying current with advancements in algorithms and tools like Python and TensorFlow.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior machine learning researcher or executive role, often involving advanced skills in deep learning, data analysis, and programming. These roles usually require extensive experience, specialized knowledge, and may include leadership responsibilities or research contributions in cutting-edge AI projects.
More about Full Time Machine Learning Researcher jobs
What are the most commonly searched types of Machine Learning Researcher jobs? The most popular types of Machine Learning Researcher jobs are:
What job categories do people searching Full Time Machine Learning Researcher jobs look for? The top searched job categories for Full Time Machine Learning Researcher jobs are:
Full Time Machine Learning Researcher jobs near you
Infographic showing various Full Time Machine Learning Researcher job openings in the United States as of June 2026, with employment types broken down into 62% Part Time, and 38% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $113,102 per year, or $54.4 per hour.

Machine Learning Researcher

Inference

San Francisco, CA • On-site

$250K - $350K/yr

Full-time

Posted 13 days ago

Job description

Help us push the boundaries of what's possible in LLM post-training. If you love training models, exploring new architectures, running experiments, and turning research insights into products that ship, we'd love to meet you.
About Inference.net
Inference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT-5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end-to-end: distillation, training, evaluation, and planet-scale hosting.
We are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high-impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high-agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates.
About the Role
You will be responsible for conducting research into experimental models, training systems, and modalities to create novel products for our customers. Your work will span from exploring new architectures and learning methods to optimizing latency and efficiency, with the goal of delivering better models to customers.
Your north star is pushing the frontier of what's possible in LLM post-training. You'll explore new techniques, run rigorous experiments, and when something works, help bring it into production with the help of your teammates. This includes training models for customers and running evaluations as part of validating your research. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to explore ambitious ideas and ship the ones that work.
Key Responsibilities
  • Research and experiment with new model architectures to improve quality, efficiency, or capability
  • Explore methods to decrease inference latency and improve serving efficiency
  • Run experiments with new learning methods, including novel approaches to SFT, RLHF, DPO, and other post-training techniques
  • Perform reinforcement learning research to improve model alignment and capability
  • Develop and improve our distillation pipeline for training high-quality models from frontier teachers
  • Train models for clients and run evaluations to validate research findings in production settings
  • Create robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance
  • Stay current with ML research and identify techniques that can improve our platform
  • Collaborate with applied engineers to bring successful research into production systems
  • Document findings and share knowledge with the team

Requirements
  • 3+ years of experience training AI models using PyTorch
  • Deep understanding of transformer architectures, attention mechanisms, and model internals
  • Hands-on experience with post-training LLMs using SFT, RLHF, DPO, or other alignment techniques
  • Experience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Megatron, TRL, or similar)
  • Strong experimental methodology, including ability to design, run, and analyze rigorous experiments
  • Track record of implementing ideas from recent ML papers
  • Experience training on NVIDIA GPUs at scale
  • Strong foundation in ML fundamentals: optimization, loss functions, regularization, generalization

Nice-to-Have
  • Publications in ML venues
  • Experience with model distillation or knowledge transfer
  • Experience with LLM speed optimization techniques
  • Familiarity with vision encoders, multimodal models, or other modalities
  • Experience with distributed training and infrastructure at scale
  • Contributions to open-source ML projects

You don't need to tick every box. Curiosity and the ability to learn quickly matter more.
Compensation
We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $250,000 - $350,000, plus equity and benefits, depending on experience.
Equal Opportunity
Inference.net is an equal opportunity employer. We welcome applicants from all backgrounds and don't discriminate based on race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or veteran status.
If you're excited about pushing the boundaries of custom AI research, we'd love to hear from you. Please send your resume and GitHub to amar@inference.net and/or here on Ashby.

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