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Remote Research Engineer Jobs in California (NOW HIRING)

Waabi

Research Engineer, World Models

San Francisco, CA · On-site +1

$155K - $269K/yr

As a Research Engineer in the World Models team, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments. This role ...

Waabi

Research Engineer, World Models

San Francisco, CA · On-site +1

$155K - $269K/yr

As a Research Engineer in the World Models team, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments. This role ...

Waabi

Research Engineer, World Models

San Francisco, CA · On-site +1

$155K - $269K/yr

As a Research Engineer in the World Models team, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments. This role ...

Waabi

Research Engineer, Neural Rendering

San Francisco, CA · On-site +1

$134K - $235K/yr

To learn more visit: www.waabi.ai As a Research Engineer in Neural Rendering, you will create the next generation of multi-sensor rendering systems for autonomous driving. You will collaborate with ...

Waabi

Research Engineer, Neural Rendering

San Francisco, CA · On-site +1

$134K - $235K/yr

To learn more visit: www.waabi.ai As a Research Engineer in Neural Rendering, you will create the next generation of multi-sensor rendering systems for autonomous driving. You will collaborate with ...

Waabi

Research Engineer, Neural Rendering

San Francisco, CA · On-site +1

$134K - $235K/yr

To learn more visit: www.waabi.ai As a Research Engineer in Neural Rendering, you will create the next generation of multi-sensor rendering systems for autonomous driving. You will collaborate with ...

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How much do remote research engineer jobs pay per year?

As of Jun 16, 2026, the average yearly pay for remote research engineer in California is $104,624.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,600.00 and $102,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Remote Research Engineer position, and why are they important?

A Remote Research Engineer typically needs a strong background in scientific research methods, programming, data analysis, and a relevant degree in engineering or a related field. Familiarity with statistical software, cloud-based collaboration tools, and experience with programming languages such as Python or MATLAB are often required, with additional certifications in machine learning or data science considered advantageous. Excellent written and verbal communication, problem-solving abilities, and self-motivation are key soft skills for success in remote environments. These competencies enable effective independent work, high-quality research output, and seamless collaboration within distributed teams.

What is a Remote Research Engineer job?

A Remote Research Engineer is a professional who conducts research and develops new technologies, algorithms, or solutions while working remotely. They typically work in fields like artificial intelligence, machine learning, software development, or scientific research. Their responsibilities include designing experiments, analyzing data, and collaborating with teams using digital communication tools. This role requires strong problem-solving skills, self-motivation, and proficiency in programming or research methodologies. Remote Research Engineers often contribute to cutting-edge advancements while maintaining flexibility in their work environment.

What are some unique challenges faced by Remote Research Engineers and how can they be overcome?

Remote Research Engineers often encounter challenges related to collaborating across different time zones, ensuring clear communication, and maintaining access to necessary data or computational resources. To overcome these issues, it's important to leverage project management tools, establish regular virtual meetings, and proactively document and share research findings with the team. Strong time management and self-discipline are also essential to balance deep-focus research tasks with collaborative discussions. Organizations usually provide virtual platforms and cloud infrastructure to support seamless work, but developing personal workflows for communication and resource access can further enhance effectiveness in the role.

What are the most commonly searched types of Research Engineer jobs in California? The most popular types of Research Engineer jobs in California are:
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Remote Research Engineer jobs near you

Machine Learning Research Engineer (MLRE) - Research

Achira

San Francisco, CA • On-site, Remote

Other

Posted 11 days ago

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.

Apply