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Credit Operations Analyst
London
£50,000 to £70,000

This is an opportunity to join a growing, data-led financial services business where you will directly influence how credit decisions are made at scale. The role offers strong exposure across credit, data, and product, with a clear focus on automation and impact.

The Company
They are a fast-growing, technology-driven financial services organisation focused on improving how consumers manage their finances. The business takes a data-first approach, using modern tools and customer insights to inform decision-making. With continued growth and investment in their data capabilities, they are building out a high-performing analytics and credit function.

The Role
You will work at the intersection of credit risk, data, and decisioning, helping to improve and automate lending processes.

• Develop and enhance automated credit decisioning and underwriting logic
• Analyse lending performance and identify opportunities to improve accuracy and efficiency
• Collaborate with product, engineering, and credit teams to reduce manual processes
• Use customer and transactional data to inform lending decisions
• Support improvements in affordability assessment and decision frameworks
• Contribute to building a scalable, data-driven credit operation

Your Skills & Experience

• Strong commercial experience in credit risk within a lending environment
• Experience working on underwriting, decisioning, or credit policy using data
• Strong SQL skills for data analysis and manipulation
• Ability to translate data insights into practical lending decisions
• Solid understanding of how credit decisions are made
• Exposure to affordability assessment or open banking data is beneficial

What They Offer

• Salary between £50,000 and £70,000
• Share options
• Private medical insurance
• Statutory pension
• Opportunity to join a growing business with strong career development potential

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Staff Engineer (AI)
£150,000 – £170,000 + benefits
London (Hybrid)
This is a great opportunity to join a high‑growth, PE‑backed organisation where you can take ownership of building and scaling an AI‑native data platform from the ground up.

THE COMPANY:

The group’s mission is to unify their businesses into a single, powerful data platform, creating comprehensive global datasets. With strong funding and a clear acquisition strategy, they are building a category‑defining B2B data business.

THE ROLE:

You will take ownership of building and rapidly iterating on core platform features as part of a highly agile engineering team. Key responsibilities include:

• Developing and shipping working prototypes within days
• Building and improving data platform components and internal tools
• Turning complex data assets into user‑ready products
• Collaborating with senior engineers and leadership on architecture decisions
• Helping modernise and scale early‑stage prototypes into production systems

YOUR SKILLS AND EXPERIENCE:

You will bring strong capability in:

• Strong experience in Python, SQL, and TypeScript
• Hands‑on software engineering experience
• Proven ability to build and deliver products quickly in fast‑paced environments
• Experience working with data‑heavy systems or platforms
• Solid understanding of modern engineering practices

THE BENEFITS:

You will receive a salary of £150,000 – £170,000 depending on experience, along with a comprehensive benefits package and the opportunity to shape a high‑impact AI platform from day one.

HOW TO APPLY:

Please register your interest by sending your CV to Molly Bird via the apply link on this page.

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Machine Learning Scientist
USA (Remote)
$180,000 to $210,000 base + bonus + equity

This is an opportunity to join a high-impact Machine Learning team where experimentation, ownership, and real-world impact sit at the core of the role. You will work on systems that directly influence commercial outcomes at scale, with the autonomy to design, test, and deploy solutions that power critical decision-making.

The Company

This organisation operates at the intersection of data, commerce, and risk, building advanced machine learning systems to solve complex transactional challenges. Their platform processes high volumes of real-time decisions, using AI to optimise customer experiences while managing risk. Machine learning is central to their product and growth, not a supporting function, giving teams strong visibility across the business.

They foster a culture that values scientific thinking, creativity, and engineering rigour, with teams structured to own their services end to end.

The Role

You will contribute to the development of production-grade machine learning systems, working across the full lifecycle from experimentation through to deployment.

* Designing and implementing machine learning models that drive real-time decisioning
* Building and optimising scalable ML pipelines to enable rapid experimentation
* Exploring and integrating new ML techniques to improve model accuracy and scalability
* Collaborating closely with Product, Engineering, and Risk teams
* Owning projects end to end, with full accountability for outcomes in production

Your Skills and Experience

* Strong commercial experience building and deploying machine learning models from first principles
* Hands-on experience working in distributed computing environments such as Spark
* Proficiency in Python and SQL for data processing and model development
* Experience designing and running experiments rather than relying on pre-built models or APIs
* Ability to translate business problems into scalable machine learning solutions

What They Offer

* Competitive base salary up to $210,000 depending on experience
* Annual bonus / equity package
* Fully remote working within the USA
* Flexible working and unlimited paid time off
* Generous parental leave and learning and development support
* The opportunity to work on high-scale systems with meaningful business impact

How to Apply

If you are interested in applying your machine learning expertise to real-world challenges at scale, please submit your CV to learn more.

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Senior Machine Learning Engineer – Animation & Real-Time Systems

We are partnering with a Series C startup at the forefront of animation and gaming technology, building next-generation 3D avatar systems powered by machine learning. This team is redefining how characters are created, animated, and brought to life in real-time environments.

We are seeking a Senior Machine Learning Engineer with strong experience in animation systems and real-time integration. This role sits at the intersection of ML model development and production-grade runtime systems, with a heavy focus on deploying intelligent animation behaviors into interactive pipelines.

Responsibilities

• Design and develop ML-driven systems for 3D avatar generation, including skeletal structures, rigging, and motion synthesis

• Build and integrate models for motion prediction, gesture generation, and animation control into real-time pipelines

• Translate model outputs into production-ready systems within Unity or Unreal Engine environments

• Develop and optimize runtime systems for animation playback, motion matching, and behavior orchestration

• Work with mocap data pipelines, including ingestion, cleaning, and model training

• Collaborate closely with graphics engineers, technical artists, and gameplay teams to ensure seamless integration

• Contribute across the ML lifecycle, with approximately 25% focused on training and fine-tuning models and 75% on engineering, integration, and runtime systems

Requirements

• 5+ years of experience in machine learning engineering, graphics, or animation systems

• Strong proficiency in C++ and Python

• Hands-on experience with Unity or Unreal Engine in a production environment

• Deep understanding of animation systems, including skeletons, rigging, motion matching, and behavior trees

• Experience integrating ML models into real-time systems or interactive applications

• Familiarity with mocap data processing and animation pipelines

• Strong systems engineering mindset with the ability to optimize for performance and latency

Nice to Have

• Experience with character animation, procedural animation, or physics-based animation systems

• Background in gaming, simulation, or interactive media

• Familiarity with generative models applied to motion or animation

• Experience working with large-scale ML pipelines or real-time inference systems

Compensation & Location

• Base salary: $200,000 – $280,000 + equity

• Open to remote candidates; preference for Los Angeles or San Francisco

Why This Role

This is an opportunity to work on cutting-edge ML-driven animation systems that bridge offline model development and real-time interactive experiences. You will have direct impact on how next-generation avatars move, behave, and interact in dynamic environments.

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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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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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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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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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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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Senior Computer Vision Engineer (2D or 3D – Clinical AI)

About the Company

The mission is simple but ambitious: democratize access to high?quality cardiac care, especially in regions where cardiologists and advanced imaging equipment are scarce.

About the Role

This is a hands-on applied engineering position focused on building and deploying real clinical AI, not academic experiments. You will own production imaging models end?to?end, ensure reliability across diverse clinical settings, and work closely with clinical, regulatory, and deployment teams.

Profile A –

• Segmentation
• Localization
• Ultrasound/Echo image processing
• Signal and noise?robust imaging pipelines

Profile B – 3D Modeling

• Volumetric reconstruction
• 3D geometry and physics-informed modeling
• Computational representations of cardiac structures

Exceptional candidates may be considered for both tracks.

What You Will Own

Computer Vision Development

• Build production-grade CV models used in live cardiac workflows
• Develop 2D segmentation, 3D/4D reconstruction, and mathematical modeling systems
• Optimize models for low?latency clinical deployment
• Debug imaging pipelines across noisy, highly variable datasets

Production ML & Deployment

• Own pipelines from prototype ? production
• Support deployment engineers with real?world constraints
• Maintain model reliability across multiple international regions
• Monitor drift, performance degradation, and operational issues

What We’re Looking For

Required

• Strong experience in Computer Vision (2D and/or 3D)
• Production ML experience-beyond research or academic prototypes
• Background in biomedical or clinical imaging (ultrasound strongly preferred)
• Ability to debug, iterate, and deliver under pressure
• Experience working with deployment or ops engineering teams
• Comfort writing regulatory?oriented technical documentation
• Strong Python + PyTorch skills
• Startup mindset – bias for action, adaptability, ownership

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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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