Head of Machine Learning / Chief Technology Officer

About Caravel Bio

Caravel Bio is a platform company on a mission to make scalable protein technologies. Many proteins can do exciting things in test tubes; we’re building new systems that help those exciting things scale. We have spent the past two years developing novel protein engineering and production methods that expand protein design space, increase the number of screenable phenotypes, and strengthen the industrial relevance of products made with machine-learning guided directed evolution. Our hub-and-spoke business model leverages our proprietary high-throughput design-test-build-learn-scale platform
in verticals across industrial biotechnology — markets where synthetic proteins and new processes offer step-change performance advantages over incumbent chemistries and systems. We hold patents on our core platform, have secured significant non-dilutive government funding, and are at an early commercial stage with paying customers. Caravel’s existing team members are pioneers and tenacious thinkers and doers. We have a shared North star; a sense of stewardship and responsibility to our society that requires we solve hard problems within the very real constraints of physics, capital, and time.

The Role

We are seeking a founding Head of Machine Learning / CTO to build the computational infrastructure that will leverage our large and novel data sets to make Caravel's
*in silico*
platform scalable, predictive, and defensible. This person will facilitate the technical vision for how machine learning, protein informatics, and data systems accelerate discovery, de-risk our commercial pipeline, and creates foundational models that themselves are valuable assets built on strong data moats.

This is a senior leadership role. You will work directly with the founding team, own technical hiring, shape wet-lab equipment and automation decisions, and be the internal voice translating wet-lab biology into computable problems — and computable answers back into high-throughput experimental strategy.

What You'll Do

–   Build a computational team that accelerates research and development of real-world products. Design and build ML models and systems of models for protein engineering: sequence-function prediction, directed evolution guidance, and multi-property optimization (stability, substrate specificity, solvent tolerance, binding affinity, cycle lifetime, and more).

–   Develop and own the data infrastructure that connects experimental results across our platform and application verticals.

–   Build and maintain bioinformatics pipelines that integrate diverse types and sizes of sequence-structure-function data sets.

–   Build and grow the computational biology team as the company scales, including hiring, mentoring, and managing external collaborators or compute vendors.

–   Partner with wet-lab scientists to design ideal experimental setup and data collection for building training sets. Translate model predictions into actionable hypotheses in a rapid design-build-test-learn cycle.

–   Lead technoeconomic modeling on the computational side — informing projections, scale-up assumptions, and platform economics for investor and partner audiences.

–   Represent computational capabilities and value propositions in fundraising, grant applications, and customer conversations.

What we're looking for

Required

–   PhD or equivalent experience in computational biology, bioinformatics, machine learning, or a closely related field.

–   Demonstrated experience applying ML to protein engineering or molecular biology problems — sequence models, structure prediction, or property regression.

–   Strong software engineering fundamentals: production-level and full-stack code synthesis.

–   Experience building and maintaining biological data pipelines in resource-constrained environments.

–   Ability to communicate results to non-computational audiences — scientists, executives, and investors.

–   Track record of leading high-impact teams, shipping models or tools that influenced real experimental decisions toward positive real-world outcomes.

Strongly Preferred

–   Experience with applying machine learning to cell-free expression systems, enzyme engineering, or industrial biotechnology.

–   Familiarity with multi-property optimization for protein design.

–   Prior work in a startup or early-stage company environment; comfort with ambiguity and a builder mentality.

–   Experience with non-canonical amino acids, expanded genetic code systems, or surface display platforms is a meaningful plus.

–   Background informing or writing winning grant proposals (SBIR/STTR, DOE, DARPA) is highly valued.

Compensation & Structure

The title and competitive salary will be awarded commensurate with experience; the planned title for an ideal candidate is “Head of Machine Learning” or “Chief Technology Officer” and a competitive salary range of $225-300k with meaningful early equity. Benefits package including health coverage. Relocation to Portland, OR is expected with an expectation of working from the shared office and lab space a minimum of two days/week to facilitate organic innovation and team cohesion.

To Apply

Send a CV and a brief note on a protein engineering or biological ML problem you have worked on — what was the question, what was the model, and what happened — to hello@caravel.bio. We will review applications on a rolling basis.

A Note to Agencies

Caravel does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Caravel or its employees is strictly prohibited unless contacted directly by Caravel’s team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Caravel, and Caravel will not owe any referral or other fees with respect thereto.