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Job Title: AI Product Director

Location: SF Bay Area (Hybrid 2-3 days/week in office)

Salary: $275-290k base

Overview:

An established fintech organization is seeking an AI Product Director to lead the next phase of its enterprise-wide AI transformation program.

This is a highly visible leadership role focused on reimagining business processes through AI and scaling successful AI solutions across multiple functions. You’ll work closely with executive stakeholders, lead a growing AI product team, and oversee multiple concurrent initiatives that are already delivering measurable business value.

The ideal candidate combines strategic product leadership with hands-on execution and has played a direct role in designing and delivering AI-powered products from concept through production.

Key Responsibilities:

• Lead the product strategy and roadmap for enterprise AI transformation initiatives
• Partner with senior business leaders to identify opportunities for AI-driven process redesign
• Translate business objectives into scalable product and solution blueprints
• Oversee multiple AI product initiatives simultaneously, ensuring quality, speed, and business impact
• Manage and mentor a team of AI Product Managers
• Lead discovery efforts with business stakeholders to map current-state processes and identify transformation opportunities
• Evaluate and select external engineering partners and maintain accountability throughout delivery
• Establish quality assurance, evaluation, and governance frameworks for AI solutions
• Partner with technical teams to define success metrics, testing methodologies, and production-readiness standards
• Drive stakeholder adoption, executive alignment, and organizational change management

What We’re Looking For:

• 8-12 years of Product Management experience
• Proven track record delivering AI-enabled products from concept through production deployment
• Demonstrated experience contributing directly to AI solution design, architecture discussions, or product blueprint creation
• Experience leading multiple concurrent product initiatives in fast-paced environments
• Experience managing Product Managers or leading product teams
• Strong vendor management experience and ability to guide third-party delivery partners
• Executive-level communication, presentation, and stakeholder management skills
• Ability to balance strategic vision with hands-on execution

Nice-to-Have:

• Experience in fintech, lending, payments, risk technology, or other regulated industries
• Knowledge of AI governance, explainability, compliance, or model risk concepts
• Familiarity with agentic systems, AI evaluation frameworks, benchmark testing, and modern LLM applications
• Experience scaling AI products within high-growth technology companies

Benefits:

• Additional performance-based cash compensation potential
• Executive visibility and influence across the organization
• Opportunity to build and lead a growing AI product function
• Exposure to large-scale AI transformation initiatives with significant business impact

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Job Title: AI Product Manager

Location: SF Bay Area (Hybrid 2-3 days/week in office)

Salary: $190-210k base

Overview:

A growing financial technology organization is expanding its AI product team and seeking two AI Product Managers to lead business process transformation initiatives powered by AI.

This is not a role focused on AI experimentation or internal productivity tools. The organization already has production AI systems delivering measurable business impact and is now extending those capabilities across multiple business functions. You will own the full lifecycle of AI initiatives, from discovery and process redesign through implementation, quality control, and adoption.

This opportunity is ideal for a hands-on product manager who enjoys ambiguity, thrives in fast-moving environments, and wants to build deep expertise in AI product development while working closely with business leaders, data scientists, and engineering partners.

Key Responsibilities:

• Lead discovery sessions with business stakeholders to understand current workflows, pain points, exceptions, and opportunities for transformation
• Redesign business processes from a 0-to-1 perspective, leveraging AI to fundamentally improve outcomes rather than simply automate existing workflows
• Create detailed product requirements, design briefs, process maps, and solution specifications
• Partner with data science and engineering teams to define AI capabilities, evaluation criteria, and success metrics
• Manage external engineering partners throughout the development lifecycle and ensure delivery aligns with product requirements
• Design quality assurance and evaluation frameworks to assess AI outputs before deployment
• Coordinate pilot programs, user testing, and adoption strategies
• Measure business impact and continuously refine solutions post-launch
• Build strong relationships with stakeholders and drive alignment across technical and non-technical teams

What We’re Looking For:

• 3-5 years of experience in Product Management, Product Strategy, Product Operations, or a related discipline
• Direct experience working on AI-powered products or AI-driven business process transformation initiatives
• Experience leading discovery efforts and gathering requirements from business stakeholders and subject matter experts
• Proven ability to create specifications, requirements documents, design briefs, or other product artifacts that have been used to build and launch solutions
• Strong problem-solving skills with the ability to structure ambiguous challenges independently
• Experience balancing strategic thinking with hands-on execution
• Excellent stakeholder management and communication skills

Nice-to-Have:

• Experience within fintech, lending, payments, insurtech, healthtech, or other regulated industries
• Experience working with external vendors, consultants, or engineering partners
• Familiarity with AI evaluation methodologies, testing frameworks, or model validation approaches
• Exposure to LLM-enabled products, AI agents, or intelligent automation platforms

Benefits:

• Additional performance-based cash compensation potential
• Opportunity to join a company actively deploying AI at scale across multiple business functions
• High degree of ownership and visibility within a rapidly growing AI product organization

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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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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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