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About the Role

This is a high-visibility, high-impact individual contributor role sitting at the intersection of machine learning, clinical data science, and translational biology. You will lead the company’s drug response prediction work – one of the most consequential and technically demanding initiatives in our portfolio.

This is not a pure research role, and it is not a pure engineering role. It requires someone who can move fluidly between rigorous quantitative analysis and the realities of working with large, messy, real-world datasets – someone who can apply state-of-the-art methods without losing sight of what actually works in practice.

The Problem We’re Solving

There are thousands of drugs that work – but only for a small subset of patients, and we largely don’t know why. We are building a systematic engine of understanding between drugs and biology: one that can decode the relationship between a patient’s biology and their response to treatment, and translate that into insights that immediately improve care.

This is foundational, mission-critical work. The person in this role will directly shape how we approach this problem – the methods we use, the data we bring to bear, and the analytical frameworks we build. It is an opportunity to have genuine scientific and clinical impact.

What You Will Do

Drug Response Prediction

• Lead the design and execution of computational approaches to predict drug response across patient populations
• Develop and validate predictive models using real-world clinical data, integrating diverse data modalities
• Identify biological and clinical signals that differentiate responders from non-responders
• Translate modeling outputs into actionable biological and clinical insights

Data & Analysis

• Work extensively with real-world data (RWD) – EHR, claims, clinical trial data – at scale
• Build robust analytical pipelines that handle messy, heterogeneous, and incomplete data
• Apply appropriate statistical frameworks to ensure rigor and reproducibility
• Contribute to the development and evolution of internal data infrastructure and analysis tooling

Modeling & Methods

• Apply and adapt state-of-the-art ML methods – including causal inference, survival analysis, and multi-omics integration – to biological and clinical problems
• Balance methodological sophistication with practical performance constraints
• Evaluate trade-offs between model complexity, interpretability, and real-world utility
• Stay current with the literature and bring relevant advances into the team’s practice

Cross-Functional Collaboration

• Work closely with biologists, clinical scientists, and data engineers to design studies and interpret results
• Communicate findings clearly across both technical and non-technical audiences
• Contribute to a collaborative, intellectually rigorous team culture

What We’re Looking For

Required

• PhD in a quantitative discipline – computational biology, biostatistics, bioinformatics, computer science, physics, statistics, or a related field; open to diverse backgrounds
• Fluent in Python – comfortable writing clean, well-structured code for data analysis and modeling
• Real-world data experience – hands-on work with EHR, claims, or other large-scale clinical datasets
• Ability to work with messy data at scale – experience wrangling, cleaning, and extracting signal from imperfect data
• Strong quantitative intuition – both in modeling design and in interpreting results critically
• Industry experience – 4+ years; 6+ preferred, ideally with both large pharma/biotech and startup exposure
• Mission-driven – genuinely motivated by the opportunity to improve patient outcomes through better science

Strongly Preferred

• Experience with causal inference methods (propensity scoring, instrumental variables, difference-in-differences, etc.)
• Background in statistics, epidemiology, or biostatistics alongside ML
• Familiarity with pharmacogenomics, multi-omics, or translational biology
• Experience contributing to or extending data infrastructure and analysis frameworks
• Track record of working across interdisciplinary teams (biology, chemistry, clinical)
• Startup experience – comfort with ambiguity, ownership, and moving quickly

Background & Experience Profile

We are open to a wide range of PhD backgrounds – what matters most is strong quantitative and analytical foundations, genuine intellectual curiosity, and the ability to work rigorously with complex biological and clinical data. Prior biology experience is not required, but candidates with some exposure to biological or clinical domains will be viewed favorably.

The ideal candidate has spent time in both large pharma/biotech (where they developed rigor and depth) and a startup environment (where they developed speed and ownership). If you haven’t done both, a trajectory that moves toward increasing independence and scope is what we’re looking for.

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ML Scientist / Researcher

Oncology AI · Foundation Models · Life Sciences

Remote

About the Role

We are building foundation models trained on human tumor biology – one of the most consequential and technically demanding challenges at the intersection of AI and medicine. As an ML Scientist, you will be a core research contributor designing and training these models across multimodal omics datasets, partnering closely with biologists and fellow research scientists to advance the state of the art in oncology AI.

This is a research-forward role for scientists who want their work to matter. We are looking for people with a track record of research excellence – those who have gone deep on model architecture, training dynamics, and rigorous experimental design. If you have built models from the ground up and published findings, we want to talk.

What You’ll Do

• Design and train large-scale foundation models on multimodal biological datasets, including genomics, transcriptomics, and other omics modalities
• Collaborate deeply with computational biologists, research scientists, and domain experts to translate biological questions into tractable modeling problems
• Drive the full research lifecycle: hypothesis formation, experimental design, model development, and rigorous analysis of results
• Contribute to agentic AI systems that reason over complex biological data
• Communicate findings internally and, where appropriate, through peer-reviewed publication

What We’re Looking For

Must-Haves

• Strong research background, typically evidenced by a PhD in machine learning, computational biology, statistics, physics, or a related quantitative field – or equivalent industry research experience
• Demonstrated ability to build and train models end-to-end, including experimental analysis and iteration
• Research excellence: first-author publications at top ML, AI, or computational biology venues are a strong positive signal
• Deep familiarity with foundation model concepts: pretraining, self-supervised learning, attention mechanisms, and large-scale training
• Comfort working at the intersection of biology and machine learning – even without a formal biology degree

Nice-to-Haves

• Experience with biological or omics data (genomics, proteomics, pathology imaging, etc.)
• Prior work in multimodal learning or multi-omics integration
• Familiarity with agentic AI systems or tool-use frameworks
• Background in oncology or disease biology

What This Role Is Not

This is not a production ML engineering or MLOps role. We are not looking for candidates whose primary experience is model deployment, serving infrastructure, or engineering-heavy systems work. The emphasis here is firmly on research depth and model development.

Compensation & Location

Base Salary: $250,000 – $288,000 (depending on experience) + equity

Location: Remote-friendly; office in South San Francisco, CA

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AI Engineer
San Francisco, California

Remote

$200,000 – $220,000 + Equity

About the Company

This innovative health tech startup is working to improve the oncology drug development process by providing better patient data to drug developers as well as better access to clinical trials for patients. This series B startup is expending to meet demand.

About the Role

As an AI Engineer, you will design and deliver applied AI systems (LLMs/CV/multimodal) that automate clinical variable abstraction and clinical note generation-with repeatable validation, robust documentation, and HITL feedback loops. Heavy emphasis on data engineering and backend rigor to make models usable and efficient.

Role Responsibilities

• Build models and pipelines across EMR/EHR, imaging, clinical reports
• Translate ambiguous clinical requirements into measurable ML objectives
• Define metrics, design experiments, estimate error; review peer work
• Deliver validated AI components for abstraction/note generation; meaningfully reduce manual QA workload via HITL; standardize documentation/testing; establish performant data manipulation patterns (e.g., PySpark, SQL/Postgres) that speed iteration.

Key Requirements

• Python
• Pytorch or Tensorflow
• Data engineering: PySpark, SQL, Postgres, query tuning, data modeling
• Cloud data platforms (e.g., Databricks, S3/Snowflake/Azure/GCP)
• Nice to have experience in oncology/biotech/health tech

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Qualitative Team Lead

Nottingham (Hybrid, 3 days per week)

£80,000 to £100,000

This is a rare opportunity to lead and shape a qualitative insight function within a business that seamlessly blends creative, strategy, and research. You will have the autonomy to define the qualitative offering while working on high-profile, global brands, with clear visibility at senior leadership level.

The Company

They are a well-established, large-scale creative and brand communications business with a significant in-house insight and strategy capability. Operating a collaborative, integrated model, they bring together creative, data, and research specialists to deliver impactful work for major international clients. The organisation is investing in growing its insight function, with qualitative research playing a central role in that evolution. You will join an environment that values curiosity, innovation, and commercial impact.

The Role

You will take ownership of the qualitative research function, leading both delivery and strategic direction. Responsibilities include:

• Leading and developing a team of qualitative researchers, with responsibility for hiring, coaching, and performance
• Designing and delivering high-quality qualitative research projects across a range of methodologies
• Partnering with account and strategy teams to support key client relationships and drive growth
• Playing a central role on major client accounts, contributing to insight-led decision making
• Introducing and scaling new methodologies, including ethnography, semiotics, and digital or UX research
• Contributing to proposals, pitches, and broader business development activity
• Ensuring high standards of storytelling, presenting insight that drives action
• Collaborating across creative, strategy, and data teams within a matrix structure

Your Skills and Experience

To succeed in this role, you will bring:

• Strong commercial experience in qualitative research within an agency environment
• Proven ability to lead, mentor, and grow a team or practice area
• Deep expertise across qualitative methodologies, with a creative and flexible approach
• Experience working with large, complex or global brands
• Confidence in senior client engagement and stakeholder management
• Commercial awareness, including involvement in proposals, pitching, or account growth
• Interest in innovation, with exposure to areas such as behavioural science, cultural insight, or emerging qual techniques

What They Offer

• Opportunity to shape and grow a qualitative function with genuine autonomy
• Exposure to senior leadership and involvement in strategic decision making
• Strong existing client base with high-profile global work
• Clear progression and the chance to expand influence across insight, strategy, and creative

How to Apply

If you are interested in leading a high-impact qualitative function in a collaborative and creative environment, please apply with your CV.

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Machine Learning Scientist

Remote (USA only)

About the Role

A frontier AI-driven bio-tech company is hiring a Machine Learning Scientist to help develop next-generation foundation models at the intersection of AI and biology.

This is an individual contributor role for a researcher who enjoys taking ideas from concept to experimentation, working on challenging problems, and collaborating with a highly technical team.

Responsibilities

• Design, train, and evaluate state-of-the-art machine learning and foundation models.
• Rapidly prototype and test new research ideas.
• Develop benchmark tasks and evaluation frameworks.
• Collaborate closely with researchers, scientists, and engineers.
• Contribute to publications and technical presentations.

Ideal Background

• PhD in Computer Science, Machine Learning, AI, Physics, Mathematics, Computational Neuroscience, or a related quantitative field.
• Strong publication record at leading conferences such as NeurIPS, ICML, ICLR, CVPR, or equivalent venues.
• Experience building models in PyTorch and conducting original machine learning research.
• Background in foundation models, LLMs, computer vision, multimodal learning, generative AI, robotics, or scientific machine learning.

Biology experience is a plus but not required. Exceptional researchers from computer vision, language modeling, robotics, autonomous driving, and other highly quantitative fields are encouraged to apply.

Why Join?

• Work on cutting-edge machine learning research with real-world impact.
• Collaborate with world-class researchers and scientists.
• Help shape the future of AI-driven scientific discovery.

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