Founding ML Engineer
Our client is an early-stage biotechnology company developing machine learning technologies to model complex biological systems and support the development of therapeutics.
Operating at the intersection of machine learning and computational biology, the company is developing computational approaches designed to make biological development more predictive, scalable, and data-driven.
The company offers the opportunity to build core technology from the ground up, influence fundamental technical decisions, and work closely with scientists and engineers on complex, open-ended problems.
Mission
The Founding Machine Learning Engineer will be responsible for developing the core machine learning technology used to model complex biological systems.
Working closely with the founding team and scientific stakeholders, the role will combine model development, scientific experimentation, and ML engineering, taking new approaches from research hypotheses and prototyping through rigorous evaluation and scalable implementation.
Responsibilities
Machine Learning & Modeling
- Design, develop, and evaluate machine learning models for complex biological systems and processes.
- Translate biological and scientific questions into tractable machine learning problems, experiments, and measurable objectives.
- Develop modeling approaches capable of learning useful representations and predictive relationships from complex biological data.
- Explore emerging machine learning approaches and rapidly assess their potential through experimentation and benchmarking.
- Investigate model behavior, generalization, limitations, and scientific relevance rather than optimizing against performance metrics alone.
Scientific ML & Experimentation
- Work closely with scientists and domain experts to understand biological problems and determine where machine learning can provide meaningful predictive capability.
- Design rigorous experiments to compare modeling approaches and validate hypotheses.
- Develop evaluation frameworks appropriate for complex scientific and biological problems.
- Work effectively with heterogeneous, sparse, or imperfect scientific data where conventional machine learning assumptions may not always apply.
- Translate experimental findings into improvements in models, data strategies, and technical direction.
ML Engineering
- Build the training, evaluation, inference, and data pipelines required to support rapid model development.
- Establish reproducible experimentation, model evaluation, testing, and versioning practices.
- Develop technical foundations that allow models and experiments to scale as datasets, computational requirements, and model complexity increase.
- Balance research velocity with the engineering quality required to turn successful experiments into robust technical capabilities.
- Build tooling that enables scientists and engineers to interact effectively with models, data, and computational workflows.
Technical Strategy & Founding Execution
- Contribute directly to the machine learning roadmap and broader technical architecture.
- Make pragmatic decisions around modeling approaches, data, infrastructure, tooling, and build-versus-buy choices.
- Take ownership of technically ambiguous problems and drive them from initial exploration to working solutions.
- Help establish engineering standards, development practices, and technical culture as the organization grows.
- Contribute to defining the future capabilities and structure of the machine learning team.
Required Qualifications
- Strong professional experience developing modern machine learning models and systems.
- Excellent programming skills in Python and experience with modern machine learning frameworks and scientific computing environments.
- Strong understanding of deep learning, model training, evaluation, experimentation, and data pipelines.
- Demonstrated ability to take machine learning problems from initial exploration through robust implementation.
- Experience designing experiments and evaluating models beyond standard benchmark metrics.
- Ability to work effectively on technically ambiguous, research-oriented problems where the solution is not predefined.
- Ability to collaborate closely with researchers, scientists, or other domain experts.
- High degree of technical ownership and comfort operating independently in an early-stage environment.
- Strong analytical, problem-solving, and communication skills.
Preferred Experience
- Experience applying machine learning to biology, computational biology, biotechnology, drug development, or other scientific domains.
- Experience with deep learning approaches applicable to complex biological or scientific data.
- Familiarity with representation learning, foundation models, generative modeling, multimodal learning, or other modern machine learning approaches relevant to scientific modeling.
- Experience working with heterogeneous, sparse, high-dimensional, or experimentally generated datasets.
- Experience building machine learning technology in a research-intensive or early-stage environment.
- Experience working at the interface between machine learning research and production-quality engineering.