Member of Technical Staff, AI Research Scientist
💰 $210,000 – $350,000/yr
At a glance
- Who can apply
- Not stated by the employer
- Work arrangement
- Remote · Remote
- Employment type
- Full-time
- Salary
- $210,000 – $350,000/yr · published by the employer
- ✓ Open to early-career candidates
Checked against the employer's source on October 14, 2026. Details come from the employer's posting; confirm them on the application page.
Job Description
Edison Scientific builds and deploys AI scientist agents to accelerate science and the development of new medicines. We are an ambitious team run by scientists and engineers from leading institutions across biology, physics, chemistry, and AI.
Role
As an Member of Technical Staff, AI Research Scientist at Edison Scientific, you play a central role in developing the models and algorithms that accelerate scientific research. Your role will encompass both cutting-edge research and applied methods, bringing your work to bear on scientific challenges in laboratories and scientists around the world.
This role is on-site at our San Francisco office in the Dogpatch neighborhood. Our office is a converted warehouse with high ceilings, open space, and a team excited about what we’re building.
Responsibilities
Interpret challenges in building AI agents for science as well-formulated statistical problems
Conduct research in post-training of LLMs on challenging scientific reasoning tasks, with the goal of exceeding expert human capacity
Conduct research in optimal sampling techniques, considering aspects like efficient exploration and quality-diversity
Own research projects end-to-end, from working with scientists to scope the problem all the way to working with engineers to deploy the outcomes
Develop and extend our experimentation platform for internal tools and projects.
Collaborate closely with a multidisciplinary team of AI researchers, engineers, biologists, and chemists, fostering an environment of innovation and discovery.
Qualifications
5-10+ years of strong track record of ML research on real-world problems in (but not limited to) reinforcement learning, representation learning, and Bayesian methods
Fluency in PyTorch, Jax, or equivalent framework
Experience with large-scale training and experimentation technologies (e.g. Ray, verl, Megatron-LM, etc.)
Experience working with data and ML systems: EDA, feature engineering, model development, model deployment, and validation in production systems.
Familiarity with modeling in PyTorch, Jax or equivalent framework.
Demonstrated experience with experimentation in academic or industry settings.
Strong programming expertise with the capability to adapt to various technical challenges in the data, ML, and LLM software stack.
Bonus points for:
PhD in Machine Learning, Computer Science, or other quantitative field
Familiarity with leveraging and managing distributed systems
Salary
$210,000 - $350,000 • Offers equity
Why join us?We're a fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it. We also offer full-time employees the following:
Competitive salary and equity
Full healthcare coverage; we pay 100% of premiums for you and your dependents
Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
Mental health support through Rula, our in-network therapist and psychiatrist network with fast availability
12 weeks of paid parental leave for maternity, paternity, and adoption
Pet care support with a yearly employer-funded stipend for your animal companions
Commuter benefits so you can pay for transit and parking with pre-tax dollars
401(k) company matching
$300 health and wellness benefit quarterly
Lunch is on us every day you're in the office, and dinner is on us when you're working late
Regular team off-sites and company events
Edison Scientific is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.
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