Remote Computational Scientist Jobs
Remote computational scientist jobs are open across the U.S. at remote-first firms and distributed research teams in sectors like pharmaceuticals, defense, climate science, and enterprise software. Employers hiring remote computational scientists right now include Artera, Colgate-Palmolive, and INTEROS. Find a role that fits below and apply directly.
Find JobsOverview
Showing 5 of 8+ Remote Computational Scientist jobs
Build ML that changes how cancer is treated.
Most medical AI stops with a publication. Our AI models guide treatment decisions today.
Artera’s AI-driven prostate test is standard-of-care in the US and cancer patients in all 50 states are using our models to help make decisions about their therapy. While we started in prostate cancer, we’ve grown into a multi-cancer platform with more disease settings in development. You'll help build what's next.
The role
At Artera, we’re making ML development agent-first. You won’t spend your days hand-writing training loops, tuning hyperparameters in notebooks, or babysitting individual runs. Instead, you’ll design, direct, and evaluate autonomous research agents that propose hypotheses, write and execute experiments, and train models autonomously. You’ll be tasked with answering specific scientific questions: which patients can safely forgo aggressive treatment or inferring molecular traits from routine digital slides and your job will be to decide what’s worth investigating and which research directions merit investment.
As a senior or staff scientist, you'll own an AI biomarker end to end, from problem framing with our product, medical, and biostatistics partners through validation, regulatory submission, and production. You'll set technical direction for the scientists and engineers around you, and take on the hardest cross-cutting problems in our field: making models hold up across scanners, stains, and sites so a patient’s result doesn’t depend on the scanner, lab, or stain behind their slide, uncovering what our models have learned about tumor biology, and building the next generation of our pathology foundation models. At the Staff level, you'll also set direction across programs and help shape the AI roadmap with leadership.
Why the work is different at Artera:
Your models help cancer patients. This isn’t AI for generating cat videos or customized emojis. Our work directly affects patients who are experiencing among the most difficult periods of their lives.
Foundation-model scale. We train our own pathology foundation models from scratch with distributed multi-node GPU training on AWS. While frontier labs may have cracked coding and math, their ability to read biology for our problems is unusable. Artera is working on the cutting edge of this space.
What you'll do
Own AI biomarker end to end, from problem framing with our product, medical, and biostatistics partners through validation, regulatory submission, and production.
Lead the next generation of our pan-cancer self-supervised pathology foundation models and the downstream models built on them (multiple-instance learning, time-to-event, segmentation), with generalization as a first-order objective.
Own the reproducibility and robustness of model predictions across scanners, institutions, staining protocols, and patient populations.
Manage coding agents to propose and execute experiments, improve the efficiency of our tooling, and identify biological insights that explain our models’ behavior.
Serve as technical lead: set technical strategy, break multi-quarter programs into milestones, manage dependencies across AI, platform, biostatistics, and clinical teams, surface risks early, and hold submission and launch dates.
Present findings to product, medical, and biostatistics teams, author and defend regulatory and quality documentation, and represent AI in design and development reviews to get models into patients’ hands.
Mentor ML scientists and engineers, and raise the bar on scientific rigor, code quality, and written communication across the team.
Publish in peer-reviewed journals, present at clinical and ML venues, and support external academic and industry collaborations.
What we're looking for:
You're an experienced ML scientist or engineer who has taken models from research into clinical or production use and has led technical programs through to launch. You bring real depth in oncology or biomarker development.
5+ years of industry experience building deep learning systems (we use PyTorch)
Hands-on experience with coding agents like Cursor or Claude Code
A track record of taking models beyond a notebook, including launching and monitoring ML products in production, and evaluating them rigorously on messy, real-world data
2+ years as a technical lead for complex ML programs: scoping problems, setting technical direction, sequencing work, and managing dependencies and risk across teams on dated deliverables
Demonstrated depth in oncology or biomarker development: cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable
Proven ability to communicate complex ML concepts to cross-functional, non-ML collaborators, including product, medical, and biostatistics teams
Experience mentoring or managing ML scientists and engineers
If you meet most of this and the work excites you, we'd love to hear from you.
Nice to have: ML on complex clinical data (medical imaging, multi-omics, longitudinal records); weakly supervised learning (e.g., MIL) and survival analysis; self-supervised representation learning (e.g., DINOv3) and adapting medical foundation models to clinical tasks; ML in regulated environments (FDA 510(k)/De Novo, SaMD, design controls, CLIA/LDT); cloud-scale training and orchestration (e.g., Flyte/Union, AWS).
You don't need: a PhD. Prior regulatory experience is a strong plus, and we'll help you build on it here.
How you'll grow: ownership of a biomarker program across the full lifecycle of a regulated clinical ML product, a direct hand in shaping our technical strategy and roadmap, and room to grow into broader technical leadership across the AI team.
Build ML that changes how cancer is treated.
Most medical AI stops with a publication. Our AI models guide treatment decisions today.
Artera’s AI-driven prostate test is standard-of-care in the US and cancer patients in all 50 states are using our models to help make decisions about their therapy. While we started in prostate cancer, we’ve grown into a multi-cancer platform with more disease settings in development. You'll help build what's next.
The role
At Artera, we’re making ML development agent-first. You won’t spend your days hand-writing training loops, tuning hyperparameters in notebooks, or babysitting individual runs. Instead, you’ll design, direct, and evaluate autonomous research agents that propose hypotheses, write and execute experiments, and train models autonomously. You’ll be tasked with answering specific scientific questions: which patients can safely forgo aggressive treatment or inferring molecular traits from routine digital slides and your job will be to decide what’s worth investigating and which research directions merit investment.
As a senior or staff scientist, you'll own an AI biomarker end to end, from problem framing with our product, medical, and biostatistics partners through validation, regulatory submission, and production. You'll set technical direction for the scientists and engineers around you, and take on the hardest cross-cutting problems in our field: making models hold up across scanners, stains, and sites so a patient’s result doesn’t depend on the scanner, lab, or stain behind their slide, uncovering what our models have learned about tumor biology, and building the next generation of our pathology foundation models. At the Staff level, you'll also set direction across programs and help shape the AI roadmap with leadership.
Why the work is different at Artera:
Your models help cancer patients. This isn’t AI for generating cat videos or customized emojis. Our work directly affects patients who are experiencing among the most difficult periods of their lives.
Foundation-model scale. We train our own pathology foundation models from scratch with distributed multi-node GPU training on AWS. While frontier labs may have cracked coding and math, their ability to read biology for our problems is unusable. Artera is working on the cutting edge of this space.
What you'll do
Own AI biomarker end to end, from problem framing with our product, medical, and biostatistics partners through validation, regulatory submission, and production.
Lead the next generation of our pan-cancer self-supervised pathology foundation models and the downstream models built on them (multiple-instance learning, time-to-event, segmentation), with generalization as a first-order objective.
Own the reproducibility and robustness of model predictions across scanners, institutions, staining protocols, and patient populations.
Manage coding agents to propose and execute experiments, improve the efficiency of our tooling, and identify biological insights that explain our models’ behavior.
Serve as technical lead: set technical strategy, break multi-quarter programs into milestones, manage dependencies across AI, platform, biostatistics, and clinical teams, surface risks early, and hold submission and launch dates.
Present findings to product, medical, and biostatistics teams, author and defend regulatory and quality documentation, and represent AI in design and development reviews to get models into patients’ hands.
Mentor ML scientists and engineers, and raise the bar on scientific rigor, code quality, and written communication across the team.
Publish in peer-reviewed journals, present at clinical and ML venues, and support external academic and industry collaborations.
What we're looking for:
You're an experienced ML scientist or engineer who has taken models from research into clinical or production use and has led technical programs through to launch. You bring real depth in oncology or biomarker development.
5+ years of industry experience building deep learning systems (we use PyTorch)
Hands-on experience with coding agents like Cursor or Claude Code
A track record of taking models beyond a notebook, including launching and monitoring ML products in production, and evaluating them rigorously on messy, real-world data
2+ years as a technical lead for complex ML programs: scoping problems, setting technical direction, sequencing work, and managing dependencies and risk across teams on dated deliverables
Demonstrated depth in oncology or biomarker development: cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable
Proven ability to communicate complex ML concepts to cross-functional, non-ML collaborators, including product, medical, and biostatistics teams
Experience mentoring or managing ML scientists and engineers
If you meet most of this and the work excites you, we'd love to hear from you.
Nice to have: ML on complex clinical data (medical imaging, multi-omics, longitudinal records); weakly supervised learning (e.g., MIL) and survival analysis; self-supervised representation learning (e.g., DINOv3) and adapting medical foundation models to clinical tasks; ML in regulated environments (FDA 510(k)/De Novo, SaMD, design controls, CLIA/LDT); cloud-scale training and orchestration (e.g., Flyte/Union, AWS).
You don't need: a PhD. Prior regulatory experience is a strong plus, and we'll help you build on it here.
How you'll grow: ownership of a biomarker program across the full lifecycle of a regulated clinical ML product, a direct hand in shaping our technical strategy and roadmap, and room to grow into broader technical leadership across the AI team.
See All 8 Remote Computational Scientist Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsRemote Computational Scientist Job Market
Who's Hiring
Top Industries Hiring
- Biotechnology & Pharmaceuticals
- Consulting & Professional Services
What Employers Look For
The qualifications that appear most often in remote computational scientist jobs.
- PhD or Master's degree in computational science, physics, chemistry, or a related field
- Proficiency in Python, C++, or Fortran for scientific computing and simulation
- Experience with HPC environments, parallel computing, and job schedulers such as SLURM
- Familiarity with machine learning frameworks applied to scientific data, such as TensorFlow or PyTorch
- Strong record of peer-reviewed publications or technical reports in a relevant domain
- Experience with version control systems and reproducible scientific workflows using tools like Git and Docker
Tips for Your Remote Computational Scientist Job Search
Show async collaboration in your portfolio
Remote computational scientist employers screen for self-direction before anything else. Document your projects with clear README files, reproducible environments, and written explanations of your modeling choices. A well-structured GitHub repository communicates remote readiness faster than a cover letter does.
Target sectors where remote modeling is standard
Pharma, defense simulation, and climate tech have normalized fully remote computational work. Focusing your search on these sectors puts you in front of employers who already have distributed research workflows and do not treat remote as an exception to manage.
Apply early to remote roles that fit
Migrate Mate lists remote computational scientist openings from across the U.S. in one place, so you can find roles that match your skills and apply directly without sorting through postings mixed with on-site work.
Prepare for technical async screening rounds
Many remote computational science interviews include a take-home problem or a code review in place of a live whiteboard session. Practice explaining your algorithmic decisions in writing as clearly as you would in a synchronous setting, since reviewers often judge your written reasoning as much as your code.
Remote Computational Scientist Jobs: Frequently Asked Questions
How do I get a remote computational scientist job?
Remote computational scientist roles go to candidates who can demonstrate deep technical fluency alongside strong async communication skills. Employers screening for remote work look for experience with version-controlled codebases, documented workflows, and tools like Jupyter, Git, and cloud computing platforms. A portfolio of reproducible analyses or published simulations gives you a concrete edge over candidates with equivalent credentials but no remote-ready proof of work.
Which companies hire remote computational scientists?
Employers currently hiring remote computational scientists include Artera, Colgate-Palmolive, and INTEROS, per current remote listings on Migrate Mate as of November 2026. The companies hiring most actively tend to be remote-first biotech and pharma firms, distributed defense contractors, and enterprise software companies running large-scale simulation or modeling teams.
Can you get a remote computational scientist job with no experience?
Yes, but remote entry-level computational scientist roles are harder to land because employers expect you to work independently from day one with minimal oversight. Smaller remote-first research firms and startups are more open to early-career candidates. A strong GitHub portfolio, a published thesis project, or open-source contributions to scientific computing tools can substitute for professional experience when you can show results directly.
Do you need a degree for remote computational scientist jobs?
Usually, yes. Most remote computational scientist postings expect at least a master's degree in physics, applied mathematics, computational biology, or a related field. Some employers will consider candidates without a formal degree if they can demonstrate equivalent skill through published work, significant open-source contributions, or a strong record of delivering computational results in a prior technical role.
Which industries hire the most remote computational scientists?
Remote computational scientist roles concentrate in Biotechnology & Pharmaceuticals and Consulting & Professional Services, based on current remote listings on Migrate Mate as of November 2026. These sectors rely on distributed modeling, simulation, and data analysis work that maps naturally onto remote team structures.
See All 8 Remote Computational Scientist Jobs
Find roles that match your experience and apply in just a few clicks.
Find Jobs