Machine Learning Scientist

About Curve Biosciences

Curve Biosciences™ (“Curve”) uses Whole-Body Intelligence to monitor chronic diseases. We’ve created the first molecular blueprint of the human body by manually curating the world’s largest collection of comprehensively-characterized tissue samples into our Whole-Body Atlas. Trained on the clarity of our atlas, our Whole-Body Intelligence models identify chronic disease states through our Whole-Body Blood tests earlier and more accurately than other methods. Our mission is to provide doctors the best intelligence for their patients and to alleviate the pain of chronic diseases by anchoring medicine in biological truth.

The opportunity

Our potential to transform chronic care is intertwined with applying our biological insights and machine learning methods to patient data. We’re seeking a Machine Learning Scientist who will collaborate closely with our computational scientists and Chief Scientific Officer to design, train, and deploy novel models for understanding disease biology from next-generation sequencing (NGS) data. To excel in this role, you possess strong technical expertise in the statistical and mathematical fundamentals of AI/ML tools, exceptional communication skills, and a cultural fit that embraces teamwork, adaptability, and innovative thinking. You will operate at the nexus of machine learning research and real-world deployment, developing models that inform clinical and experimental decisions. This position is ideal for a proactive, detail-oriented professional who is eager to quickly integrate into a collaborative team, leverage existing knowledge, rapidly acquire new skills, and iteratively develop impactful models using in-house datasets.

Your responsibilities:

• Design, train, and evaluate machine learning models (classical, deep learning, and hybrid) on large-scale NGS datasets.

• Develop novel modeling approaches for extracting disease-relevant signals from high-dimensional biological data.

• Collaborate closely with computational and experimental scientists to ensure models reflect biological reality and inform assay design.

• Translate model outputs into actionable insights that guide experimental and clinical decision-making.

• Contribute to publications and present work at leading AI/ML venues (e.g., NeurIPS, ICML, ICLR, AAAI, and similar).

• Write clean, reproducible, and well-tested code following best practices in scientific computing.

• Promote a culture of scientific integrity, growth, transparency, collaboration, mutual respect, and fun, while contributing to our goal of improving patient lives.

Personally you are:

• A collaborative, inclusive team player who thrives in interdisciplinary environments.

• A clear and thoughtful communicator, comfortable explaining complex ideas across disciplines.

• Curious, impact-driven, and motivated by scientific discovery.

• Humble, open-minded, and eager to learn.

• Passionate about applying data and science to improve patients’ lives.

Expected qualifications:

• PhD in Computer Science, Statistics, Computational Biology, or a related quantitative field (or Master's with equivalent research experience).

• Track record of first-author or significant contributions to publications applying machine learning to biological or biomedical data.

• Strong hands-on experience with PyTorch and the scientific Python ecosystem (NumPy, SciPy, Pandas, etc.).

• Demonstrated experience designing, training, and rigorously evaluating deep learning models (e.g., ablation studies, failure analysis, and interpretability studies).

• Strong intuition for modeling tradeoffs, including when to apply simpler vs. more complex methods.

• Experience with or knowledge of ML infrastructure engineering best practices, including GPU-based training workflows.

• Experience with cloud environments (e.g., GCP, AWS), high-performance computing, and version control (Git/Github).

• Able to effectively perform scientific literature reviews that drive insights.

• Experience contributing to and maintaining deep-learning codebases, with a high bar for engineering quality, reproducibility, and testing.

• Ability to work from our San Mateo office at least 2-3 days per week.

Nice to have:

• Experience in building DL models for genomic data, with knowledge of state-of-the-art genomic foundation models.

• Experience training, scaling, and evaluating large models on applied, real-world problems, including building reliable evaluation suites and diagnosing failure modes.

• Familiarity with the tools, data formats, and workflows commonly used in computational biology.

• Experience working in an interdisciplinary scientific environment, where you are able to inject domain-specific inductive biases into models.

What we offer:

Curve Biosciences is dedicated to improving patients’ lives and supporting our employees’ dreams. Some of our employee benefits include:

• Opportunity to make a real-world impact by building tools and models that will deliver disease monitoring insights to real patients in the immediate-near term.

• Significant ownership and independence, with responsibility for driving projects from concept to deployment.

• Collaborative environment with no silos. Your data and modeling insights will link directly to the lab team’s decision making.

• Beautiful, modern office space in San Mateo featuring lunches, snacks, drinks, and on-site fitness facilities.

• Salary Range: $130,000 to $150,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience.

• Substantial equity ownership and annual performance bonus.

• Flexible PTO, comprehensive health coverage (medical, dental, and vision), and 401(k) plan.

• Training and support from successful leadership (past companies’ total value: ~$30B).

• Opportunities to attend and present research at AI/ML conferences.
  

Curve Biosciences is an equal opportunity employer. We highly value diversity at our company and do not discriminate on the basis of race, religion, color, national origin, gender, age, sexual orientation, marital status, veteran status or disability status.