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Radical Numerics
Actively Hiring
Next-generation AI lab for general biological intelligence
  • Top 10% of responders
    Radical Numerics is in the top 10% of companies in terms of response time to applications
  • Responds within a week
    Based on past data, Radical Numerics usually responds to incoming applications within a week
  • Growing fast
    Showed strong hiring growth in the past month

Applications Engineer

Posted: 1 week ago• Recruiter recently active
Job Location
Visa Sponsorship

Not Available

RelocationNot Allowed

About the job

About Us

Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the code of life, and our purpose is to reduce human suffering.

Our team created Evo, and started the field of generative genomics. Our work was featured on the cover of Science, and presented by our CEO on the main stage of TED2025. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome from scratch. Evo 2, featured in Nature, is the largest fully open source AI project across any domain.

Radical Numerics is bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We’ve redesigned the foundation model training stack to turn the world’s raw scientific data (e.g. biological sequences, experiments, and physical processes), into intelligible, generative models that can expand and accelerate what humanity can understand, design, and cure.

The same generative breakthroughs that enable life-saving cures also lowers the barrier to creating engineered threats and AI-generated bioweapons. We believe these forces are inseparable. Radical Numerics was founded to develop both the power to design and the responsibility to defend.

About the Role

You build what our partners use. Our models produce variant scores; you build everything between those scores and the scientist who needs to act on them.

The science team owns the model itself. You own the pipelines that run it on partner data, the interfaces people use to work with the results, and the evaluations that demonstrate how the scores hold up. Our inference infrastructure and core API are out of scope for this role.

You'll be paired with a product lead who owns a customer lane. You'll present your own work directly in partner meetings, which means fielding technical objections yourself. Expect to ship on a weekly cadence.

What You'll Do

  • Develop the application surface for your area of ownership, including interactive workbenches, variant scoring and ranking pipelines, and other tools that make model output usable by scientists unfamiliar with our API.
  • Run our models on partner data and present results with enough interpretability for a computational biologist to trust the output.
  • Build the evaluation infrastructure that makes our claims verifiable, including held-out benchmarks, blind retrospectives on solved cases, and head-to-head comparisons against tools partners currently use.
  • Present your own work directly in partner meetings, field technical objections in real time, and return with fixes.
  • Ship on a weekly cadence. Most features begin as prototypes developed live in partner conversations.

What We're Looking For

  • You build across the stack. Python for data and model work, TypeScript, React or similar for the interfaces.
  • You have worked with real genomic data. VCFs, reference builds, variant annotation, and the specific ways this data is messy in practice.
  • Comfortable engaging a partner's computational biology team as a technical peer, including in skeptical or challenging technical discussions.
  • Comfortable operating without a finished spec, and able to take a rough direction and a partner conversation and turn them into a working system within a week.

Nice to Have

  • Experience designing model evaluations, and an opinion about what makes a benchmark honest.
  • You've handled sensitive human data under strict access controls and understand the limits that comes with.
  • A forward deployed engineering, solutions engineering, or founding engineer background at an AI or infrastructure company.

Radical Numerics is committed to equal employment opportunity and does not discriminate in any employment opportunities or practices based on an individual's race, color, creed, gender (including gender identity and gender expression), religion (all aspects of religious beliefs, observance or practice, including religious dress or grooming practices), marital status, registered domestic partner status, age, national origin or ancestry (including language use restrictions and possession of a driver’s license issued under California Vehicle Code section 12801.9), natural hair, physical or mental disability, political affiliation, medical condition (including cancer or a record or history of cancer, and genetic characteristics), sex (including pregnancy, childbirth, breastfeeding or related medical condition), genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.

Radical Numerics participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.

About the company

Radical Numerics company logo

Radical Numerics

Actively Hiring
Next-generation AI lab for general biological intelligence11-50 Employees
Company Size
11-50
Company Type
Artificial Intelligence
Company Type
Biotech
  • Top 10% of responders
    Radical Numerics is in the top 10% of companies in terms of response time to applications
  • Responds within a week
    Based on past data, Radical Numerics usually responds to incoming applications within a week
  • Growing fast
    Showed strong hiring growth in the past month
Learn more about Radical Numerics image

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