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Machine Learning Research Engineer Jobs (NOW HIRING)

Profluent

Machine Learning Research Engineer

Emeryville, CA · On-site +1

$237K/yr

We're looking for an experienced Machine Learning Engineer to build and improve the models and ML ... Partner with ML and protein design scientists to prototype research ideas and bring them into ...

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How much do machine learning research engineer jobs pay per year?

As of Sep 13, 2026, the average yearly pay for machine learning research engineer 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 does a machine learning research engineer do?

A Machine Learning Research Engineer develops and improves machine learning models, conducts research to advance AI techniques, and implements scalable algorithms. They work at the intersection of applied research and engineering, leveraging mathematical and statistical methods to optimize performance. Their role involves experimenting with new architectures, analyzing large datasets, and collaborating with data scientists and software engineers to deploy models into production.

What are the key skills and qualifications needed to thrive as a machine learning research engineer?

A Machine Learning Research Engineer typically needs a strong background in computer science, mathematics, and statistics, often with a graduate degree in a related field. Proficiency in programming languages such as Python or C++, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with tools for data analysis are crucial, along with relevant certifications being a plus. Strong problem-solving skills, collaboration, and effective communication help drive innovative research and facilitate teamwork. These competencies are essential for developing advanced machine learning models, staying current with evolving technologies, and effectively translating research into real-world applications.

What are some common challenges faced by machine learning research engineers in their daily work?

Machine Learning Research Engineers often encounter challenges such as sourcing and preparing large, high-quality datasets, tuning complex model architectures, and ensuring reproducibility of experimental results. They work closely with cross-functional teams, including data scientists and software engineers, to deploy models in production environments and must frequently adapt to rapidly evolving research. Keeping up with the latest scientific literature and integrating new algorithms into ongoing projects can be demanding but is also rewarding. This collaborative, fast-paced environment provides constant opportunities for learning and professional development.

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Machine Learning Research Engineer jobs near you
Infographic showing various Machine Learning Research Engineer job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $106,012 per year, or $51 per hour.

Senior Machine Learning Research Engineer

San Francisco, CA • On-site

AI Talent Now
See more jobs at AI Talent Nowexternal

$123K - $169K/yr

Other

Posted 6 days ago

Job description

Senior Machine Learning Research Engineer

San Francisco, United States | Posted on 09/03/2026

AI Talent Now, LLC is a powerhouse in direct-hire talent acquisition, connecting exceptional talent with industry-leading organizations across IT, Engineering, Financial Services & Fintech, and Manufacturing & Robotics. Headquartered in Atlanta, Georgia, we proudly serve clients and candidates nationwide.

Job Description

AI Talent Now job # ZR 104

We are looking for a Machine Learning Research Engineer with 5+ years of experience (PhD + 1 year industry, or 5 years industry with publications) to own the full lifecycle of ML research and development at the frontier of audio AI. You'll join a fast-growing team backed by NVIDIA and top-tier investors, building cutting-edge speech and audio models that power the data layer for the world's leading AI labs. This is a role for someone who thrives in a research-driven environment, has published at top conferences, and is excited to take end-to-end ownership of novel ML directions — from design and training to deployment and fine-tuning.

What will you be doing?

  • Owning full research directions end-to-end — designing, training, deploying, and fine-tuning ML models for speech and audio applications
  • Publishing and advancing the state of the art in speech, audio, and multimodal ML (NeurIPS, ICML, ICLR caliber work)
  • Building production-grade inference systems and resilient pipelines that process terabytes of audio data daily
  • Collaborating cross-functionally with operations and engineering teams to gather training/evaluation datasets and improve model quality
  • Setting ML roadmaps and mentoring other engineers as the team scales
Requirements
  • Must have
  • 1+ years in industry with full lifecycle ML ownership: design, train, deploy, fine-tune
  • Education:
  • PhD from a top-25 CS program (or 5+ years industry ML research experience without PhD)
  • Baseline
  • Seniority:
  • 5 - 15 years of experience in ML research engineering, with focus on speech/audio/multimodal models using Python and PyTorch
  • Experience at a high-growth startup
  • Research in speech, audio, multimodal, text-to-speech, or related domains
  • Strong Python and PyTorch with production deployment experience
  • Excited to own a research direction end-to-end independently
  • Based in or willing to relocate to San Francisco (in-office)
  • At least 1 year of industry experience (post-PhD)
  • Nice-to-have
  • Experience at a top AI lab or research-focused startup (e.g., DeepMind, xAI, Anthropic, OpenAI, Meta FAIR)
  • Mix of big company and startup experience
  • Education
  • Many publications, especially at NeurIPS, ICML, or ICLR in speech, audio, multimodal, or related areas
  • Many publications, especially at NeurIPS, ICML, or ICLR in speech, audio, multimodal, or related areas
  • Updated
  • Experience training large neural network models
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