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The Role

We are looking for a Technical Architect to take joint ownership of two critical dimensions: shaping the architecture of our platform and providing hands-on technical leadership to our engineering team. You will be a key decision-maker in how we build, scale, and evolve our systems – balancing technical excellence with practical delivery.
You will work closely with engineering, AI/ML, and product teams, and you will have a direct impact on the technology roadmap of a fast-growing startup in a highly regulated and meaningful space.

Responsibilities

Systems Architecture

• Design and evolve the overall platform architecture – backend services, ML inference pipelines, and clinical integrations – ensuring scalability, security, and performance.
• Define and enforce architectural patterns, coding standards, and technology decisions with a long-term perspective.
• Evaluate and select technologies, frameworks, and tools, clearly articulating trade-offs to both technical and non-technical stakeholders.
• Ensure architecture decisions account for the regulatory requirements of the medical device space (MDR, FDA SaMD).
• Lead the technical design of integrations with hospital systems (PACS, HIS) using standards such as DICOM and HL7/FHIR.
• Own non-functional requirements: reliability, latency, data privacy (GDPR, HIPAA-aligned), and disaster recovery.

Technical Leadership

• Be the technical reference for the engineering team: code reviews, mentoring, and establishing engineering best practices.
• Collaborate with the AI/ML team on the productionisation of inference models and the optimization of the end-to-end ML pipeline.
• Contribute to sprint planning and technical estimation, helping the team break down complexity into deliverable increments.
• Proactively identify and mitigate technical risks, communicating them clearly before they become blockers.
• Foster an engineering culture centred on quality, automation, observability, and continuous improvement.

Requirements

Must-haves

• 5-8 years of software engineering experience, with at least 2-3 years in architecture or senior technical leadership roles.
• Proven experience designing and operating cloud-native systems on AWS (compute, storage, networking, managed services, IAM).
• Strong background in Python for production environments – APIs, data processing pipelines, and ML model integration.
• Solid understanding of containerisation (Docker) and orchestration in production environments.
• Deep knowledge of distributed systems design principles: microservices, event-driven architecture, API design, and data consistency patterns.
• Experience building CI/CD pipelines and driving DevOps practices across an engineering team.
• Ability to document and communicate architecture decisions clearly, both in writing and in technical discussions.
• Comfortable operating in a startup environment: autonomous, pragmatic, and able to balance ideal solutions with real constraints.

Nice-to-haves

• Experience deploying and optimising ML models in production – including ONNX Runtime or similar inference frameworks.
• Familiarity with medical imaging standards: DICOM, HL7, FHIR.
• Background in regulated environments (MDR, FDA 21 CFR Part 11, ISO 13485, or similar).
• Experience with MLOps practices: model versioning, drift monitoring, and retraining pipelines.
• Knowledge of PHP/Laravel or experience working with and evolving an existing codebase built on it.
• Prior experience in healthtech, medtech, or other highly regulated industries.

What We Offer

• Competitive salary aligned with your experience and the market.
• Hybrid model with real flexibility – we care about outcomes, not hours.
• Direct, visible impact: your work shapes tools that help clinicians make better diagnoses.
• Small, highly skilled team with strong technical autonomy.
• Annual budget for training, conferences, and certifications.
• Freedom to challenge the status quo and propose improvements to how we build things.
• A collaborative, mission-driven environment where engineering is taken seriously.

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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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Staff Software Engineer – AI Platform · Full-Time

About the Role

We’re looking for a Staff Software Engineer to join our AI platform team at a fast-growing, healthcare-focused startup. This is a hands-on technical leadership role – you’ll be the person who dreams up what’s possible, architects the solution, writes the code, and then hands off polished specs

You won’t be managing people. You’ll be doing the work: designing agentic AI workflows, building generative AI features, and thinking creatively about what could make our platform genuinely better for the people using it. If you’ve ever been the person in the room who says “what if we just built it this way” and then goes home and actually builds it – this role is for you.

What You’ll Do

• Design, architect, and implement agentic AI and generative AI workflows that power core platform capabilities
• Take ownership from idea to implementation – whiteboard it, validate it, ship it
• Identify creative, non-obvious opportunities to leverage AI across the platform and bring those ideas to life
• Contribute to marketing technology initiatives and help build commercially viable AI models

What We’re Looking For

• Proven experience building agentic AI systems or AI-powered chatbots from scratch – not just integrating APIs, but architecting the underlying system
• Strong Python skills across relevant frameworks – Django, Flask, and/or FastAPI
• Comfort with the full development lifecycle: design, implementation, testing, validation, and iteration
• Experience building and deploying on AWS
• Ability to code without relying on AI-assisted tooling – you understand what you’re writing and why
• A builder mentality – you move fast, think creatively, and take pride in shipping things that work
• Frontend experience with React is a plus, though backend depth is what matters most here

Nice to Have

• Experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar agentic frameworks
• Background in healthcare, health tech, or a similarly regulated industry
• Experience working with distributed or cross-timezone engineering teams

Why This Role

• Greenfield AI work – you’ll have real influence over what gets built and how
• Collaborate with a tight-knit, high-trust international team
• Meaningful domain – the work you do will have a direct impact on how healthcare is delivered
• Salary range up to 350k for the right level

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Senior AI/ML Engineer – Foundation Models & Agentic AI
Life Sciences AI | Biotech / Therapeutics / Computational Biology

What You’ll Work On

• Develop and train proprietary foundation models on multi-omics data (genomics, transcriptomics, proteomics, metabolomics)
• Design novel algorithms to integrate and differentiate across omics modalities
• Own the full training pipeline: data ingestion, tokenization, pretraining, and evaluation
• Push the state of the art – we are building models that do not exist yet

Agentic AI for Life Sciences

• Build and deploy AI agents that assist researchers across therapeutic development, computational chemistry, and biology
• Design multi-agent architectures using LangChain, LangGraph, and custom orchestration layers
• Integrate agents with internal databases, experimental systems, and third-party scientific tools
• Develop agentic workflows for use cases in cosmetics R&D, drug discovery, and clinical analysis

Production ML Infrastructure

• Own model deployment, scaling, and serving infrastructure
• Optimize inference using NVIDIA TensorRT-LLM, Dynamo, Triton, and related tooling
• Partner with software engineering on integration into our broader platform
• Maintain reliability, performance, and observability across deployed models

What We’re Looking For

Required

• Strong hands-on ML/AI engineering experience – you write real code, not just direct others
• Deep proficiency in Python and PyTorch – this is non-negotiable
• Experience training, fine-tuning, and deploying foundation models from scratch or from pretrained checkpoints
• Familiarity with the Hugging Face ecosystem (Transformers, Datasets, PEFT, Accelerate)
• Experience with LangChain and/or LangGraph for agentic pipeline development
• Working knowledge of the NVIDIA stack: TensorRT-LLM, Triton Inference Server, Dynamo
• Comfort with large-scale distributed training tools: DeepSpeed, xFormers
• Strong understanding of state-of-the-art model architectures (transformers, SSMs, diffusion, etc.)
• Ability to collaborate across technical and non-technical teams – bio, chem, software, clinical
• Excellent communication skills – you can explain a training run to a chemist

Strongly Preferred

• Experience with omics data (any modality: genomic, proteomic, transcriptomic, metabolomic)
• Background in computational biology, computational chemistry, or bioinformatics
• Prior work in biotech, pharma, or life sciences AI (not required but a plus)
• Experience building or contributing to multi-agent systems in a production environment
• Track record of independent research or open-source contributions in ML

Technical Skills Summary

Core ML / Training

• PyTorch – primary framework
• DeepSpeed – distributed training and ZeRO optimization
• xFormers – memory-efficient attention and transformer components
• Hugging Face Transformers, PEFT, Accelerate
• Training and fine-tuning large language and multimodal models

Inference & Deployment

• NVIDIA TensorRT-LLM – high-performance LLM inference
• NVIDIA Dynamo – inference orchestration and scheduling
• Triton Inference Server – model serving and batching
• Model quantization, distillation, and latency optimization

Agentic & Orchestration

• LangChain / LangGraph – agent design and multi-agent orchestration
• Tool-use, RAG pipelines, and memory systems
• API and system integration across scientific data sources

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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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Clinical Data Scientist
San Francisco, California

Remote

$160,000 – $190,000 + 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 a Clinical Data Scientist, you’ll execute the last leg of the clinical data pipeline by transforming, cleaning, validating, and delivering high‑quality clinical datasets for pharma partners. Heavy collaboration with Clinical Ops, AI Engineering, and Data Delivery.

Role Responsibilities

• Transform raw, abstracted, and AI‑processed data into CDISC SDTM/ADaM datasets
• Program statistical outputs (tables, listings, figures) in SAS/R/Python
• Investigate data anomalies across multiple messy data inputs
• Define data dictionaries and standards before study kickoff
• Handle data in a HIPAA‑aligned manner

Key Requirements

• 2-5 years in clinical data science, stat programming, or clinical data management
• Pharma/Biotech experience
• SAS, R, Python, SQL
• Real‑world clinical data or oncology trials
• CDISC SDTM/ADaM

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Manager, Software Engineering

Location: Hybrid (NYC)
Pay: $200k – $280k + Bonus + Equity

About the Role

Join a high-growth technology company building AI-powered enterprise software used by professionals to analyse, review, and validate large volumes of complex information. This leadership opportunity will oversee a highly experienced engineering team responsible for developing collaborative analysis tools that sit at the intersection of artificial intelligence and human decision-making. The role requires a strong people leader who can drive execution across multiple engineering groups while helping shape the future of an evolving AI platform.

Responsibilities

• Lead, mentor, and grow a team of senior software engineers, including hiring, performance management, and career development.
• Drive delivery of complex initiatives that require coordination across multiple engineering teams and stakeholders.
• Manage roadmap execution, ensuring projects are delivered against agreed timelines and business objectives.
• Negotiate scope, prioritise incremental releases, and leverage feature flags to deliver customer value quickly.
• Remove blockers and manage dependencies across platform, infrastructure, and product teams.
• Improve engineering resilience through documentation, knowledge sharing, design reviews, and reducing key-person dependencies.
• Participate in architecture discussions and advocate for platform capabilities required by the product roadmap.
• Partner closely with Product and Design leadership to define, prioritise, and deliver new capabilities.
• Communicate risks, delivery status, and trade-offs clearly to leadership and stakeholders.

Must-Have Qualifications

• 2+ years of direct people management experience leading software engineers.
• 8+ years of software engineering experience with strong backend development expertise.
• Hands-on experience with Python and large-scale distributed systems.
• Demonstrated success delivering complex, cross-functional initiatives involving multiple teams.
• Experience managing delivery against committed deadlines through scope management and phased releases.
• Experience leading geographically distributed engineering teams.
• Strong stakeholder management and communication skills.
• Bachelor’s degree in Computer Science, Engineering, or related field preferred.

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