ZipRecruiter

Log In

1

Machine Learning Research Engineer Jobs (NOW HIRING)

previous page

Showing results 41-60

All JobsTechnology JobsData JobsData Scientist JobsMachine Learning Research Engineer Jobs

Machine Learning Research Engineer information

See salary details

$37K

$106K

$142.5K

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.

More about Machine Learning Research Engineer jobs

What cities are hiring for Machine Learning Research Engineer jobs?

Cities with the most Machine Learning Research Engineer job openings:

What states have the most Machine Learning Research Engineer jobs?

States with the most job openings for Machine Learning Research Engineer jobs include:

What are popular job titles related to Machine Learning Research Engineer jobs?

For Machine Learning Research Engineer jobs, the most frequently searched job titles are:

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.

Machine Learning Research Engineer (MLRE) - Research

San Francisco, CA • On-site

Achira
See more jobs at Achiraexternal

Other

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Why Achira

At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.

At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.

About the Role

We're looking for a rare individual who thrives at the intersection of applied machine learning research and rigorous software engineering. You will advance the state of the art in foundation simulation models by implementing and experimenting with internal and literature-sourced ideas, participating with research teams to scale our ML systems, train and evaluate models, and engineer scientific prototypes into production.

While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities

What You’ll Do
  • Design and run experiments to test out hypotheses on the path to foundation model development.
  • Engineer meaningful evals and metrics which enable rapid model iteration.
  • Design, build and maintain scalable, reproducible libraries for training, experimentation evaluation, and simulation, in service of large-scale research initiatives.
  • Implement model architectures both from the literature and developed in collaboration with our in-house researchers that push the boundaries of molecular simulation.
  • Enable agent-driven research and workflows and maintain guardrails on agentic tooling.
  • Help prepare manuscripts, software artifacts, and datasets for public release.
About You
  • Strong software engineering fundamentals, with experience not just building one-off scripts but reproducible pipelines for research, writing necessary documentation, and observing coding best-practices.
  • Track record of observable artifacts (e.g., GitHub, papers) showing work in ML or scientific computing libraries.
  • Solid working knowledge of PyTorch and JAX and the modern ML research stack.
  • Comfortable with HPC or large-scale compute environments, and used to thinking on the scale of hundreds or thousands (or even more!) fits running at once.
  • Sufficient scientific depth to engage with the research questions, whether developed through prior industry experience or during a PhD.
Nice to Have

Even if you hit none of these bonus features, we encourage you to apply!

  • Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar).
  • Familiarity with generative modeling: diffusion models, flow matching, score-based methods.
  • Regular involvement in open-source ML or scientific computing libraries.
  • Experience building agent-driven research, active learning, and data curation pipelines.
#J-18808-Ljbffr

Apply