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About This Role
We're looking for a Scientist - AI/ML to join our research and engineering team at an early-stage start-up working at the intersection of deep learning and biology. This is a hands-on research role — you'll be designing and building deep neural network architectures from scratch, not fine-tuning off-the-shelf models or running MLOps pipelines. You'll work closely with immunologists, biochemists, and bioinformaticians to translate real biological problems into novel machine learning approaches, taking ideas from literature review and mathematical formulation all the way through to trained, deployed models. If you enjoy deriving equations as much as writing code, and want to use modern AI-assisted coding tools to move faster on genuinely hard problems, this role is built for you.
Minimum Qualification
- PhD in Computer Science, Mathematics, or a related field, with a dissertation involving Deep Neural Networks; or
- Postdoctoral research experience involving Deep Neural Networks; or
- PhD (as above) plus prior industry experience as a Machine Learning practitioner
Ideal Candidates
- PhD/Postdoctoral researchers from Computer Science or Mathematics-related departments whose research centered on Deep Neural Networks, with a strong aptitude for coding.
- Candidates with deep, hands-on Generative AI experience will be given strong preference: having designed, implemented, and trained generative models such as Diffusion, Flow Matching, Score-based, or Consistency models from the ground up, with a solid understanding of the underlying mathematics. (Experience limited to fine-tuning pre-trained models, prompt engineering, or building API-based GenAI applications will not be considered sufficient.)
- Candidates with in-depth knowledge of modern Transformer and LLM architectures (e.g., attention variants, positional encodings such as RoPE, Mixture-of-Experts, normalization and scaling choices), and hands-on experience training them and running efficient inference, including test-time compute scaling (e.g., sampling and search strategies, reasoning-time compute allocation), will be given strong preference.
- This position requires strong coding expertise using PyTorch and comfort working with AI agent-based coding tools (e.g., Claude Code, Codex, or similar) to accelerate research and engineering workflows.
- Applicants with hands-on experience in building Diffusion, Graph Neural Networks, or Point Cloud Architectures will be given higher priority.
Responsibilities
- Designing and implementing deep learning algorithms from scratch to solve unique problems
- Please note: this is a research and engineering position, not a data analytics or MLOps opening
- Writing extensive code from scratch for Training, Inference, Data Analysis, and Processing — leveraging AI agent-based coding tools where they improve speed and quality
- Understanding and deriving mathematical equations
- Conducting exhaustive literature reviews of existing machine learning and deep learning approaches
- Understanding data and refining feature sets
- Learning and adapting fast in an early-stage start-up
- Taking end-to-end responsibility for R&D, design, development, testing, and release of products
- Working in a cross-domain team with software engineers, immunologists, biochemists, and bioinformaticians
Must Have
- PhD (or postdoctoral experience) with a research focus on Deep Neural Networks
- Hands-on experience writing code in one or more programming languages (preferably Python)
- Prior experience building custom Deep Neural Networks from scratch
- Strong Software Engineering, Machine Learning, and Mathematical skills
- Prior experience with PyTorch or TensorFlow
- Familiarity with AI agent-based coding tools (e.g., Claude Code, Cursor, Copilot) and using them effectively in a research/engineering workflow
- Ability to work and communicate in a cross-domain team
- Ability to explore new problem statements
- Openness to learning and exploring new avenues
Good to Have
- Prior professional or research experience in building custom Diffusion models
- Prior professional or research experience in building custom Graph Neural Network models
- Prior professional or research experience in building custom Point Cloud Architectures
- Prior professional or research experience in building custom generative models using Flow Matching, Score-based, or Consistency model approaches
- Hands-on experience training Transformer models from scratch (not only fine-tuning), including large-scale or distributed training (e.g., FSDP, DeepSpeed, mixed precision)
- Experience with LLM inference optimization and test-time compute techniques (e.g., KV-cache optimization, quantization, speculative decoding, best-of-N sampling, tree search, verifier-guided decoding)
- Familiarity with efficient attention implementations (e.g., FlashAttention, sparse or linear attention) and long-context modeling
- Experience applying generative or Transformer-based models to scientific or biological data (e.g., protein, molecular, or sequence modeling)
- Published research (peer-reviewed papers, preprints) in relevant deep learning areas
Sourced from the fund's portfolio job board — apply on the company's site.