Senior AI Engineer, Causaly
Hybrid
3-5 days a week in office
AI for biomedical cause & effect discovery
Be an early applicant
AI for biomedical cause & effect discovery
101-200 employees
Be an early applicant
Hybrid
3-5 days a week in office
101-200 employees
Company mission
Causaly’s mission is to accelerate the pace of human discovery in biomedicine and to power the research organizations of the world’s largest life sciences companies to accelerate their drug discovery and development programs.
Role
Who you are
- MSc/PhD in computer science, machine learning or equivalent
- Experience fine-tuning LLMs for NLP tasks in industry
- Strong analytical and proven problem-solving skills
- Demonstrable industry experience delivering AI/ML frameworks for a product
- Expertise in working with ML frameworks such as PyTorch, Tensorflow, scikit-learn, Langchain
- Experience with DL architectures such as transformers/CNNs
- Excellent programming skills in Python and object-oriented paradigm
- Agile software development experience
Desirable
- Experience in Biomedical data or computational sciences
- Experience in cloud platforms such as GCP or AWS
- Experience with MLOps/LLMOps frameworks and best practices
What the job involves
- The ML Engineer will be a key addition to Causaly’s AI organisation
- You will work alongside an interdisciplinary team of experts to build scalable and robust solutions to highly complex NLP problems that drive business impact
- You will develop and implement cutting-edge machine learning algorithms to help us extract valuable insights from large biomedical datasets
- Lead the development and optimization of machine learning models and algorithms for processing and extracting insights from scientific literature
- Collaborate with Research Engineers to experiment with new ideas, evaluate models, and improve performance
- Work with the engineering team to ensure seamless integration of machine learning models into Causaly's platform, ensuring accuracy, efficiency, and scalability
- Participate in code reviews and ensure high-quality standards for all deliverables
- Stay up-to-date with the latest research and advancements in machine learning and related fields
Salary benchmarks
Our take
Our generation’s repositories of human knowledge have become massive, and digital, which is why Causaly's solution to boost research productivity has received much acclaim.
Swiss Novartis is Causaly’s first customer, quickly followed by an array of pharma and biotech companies that are leveraging the platform to accelerate their execution.
The company collaborates with strong research labs on developing its Machine Learning algorithm, including UCL, King’s College London and AUEB’s NLP group.
The company's aim is to reach human performance for understanding single sentence causality, as well as to visualise this evidence with network graphs.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 4 rounds)
Jul 2023
$60m
SERIES B
May 2021
$17m
SERIES A
Total funding: $82.7m
Company benefits
- Personal Learning Budget
- Hybrid Working Environment
- Wellbeing Allowance
- Comprehensive Private Medical Insurance
- Pension Contribution
Company values
- Ownership - A sense of personal responsibility is our team’s superpower. We go above and beyond to deliver value to our colleagues, company and customers at pace, time and time again. We take the initiative to do more, better, every day. We put in 110%, even when no-one’s looking. We are dedicated team players, who own our actions and our mistakes.
- Perseverance - Growing a company 3x every year is hard. Very hard. Processes are constantly breaking. Change is frequent. It takes effort and grit. If there is no way forward, we make one. We push through setbacks with patience and determination. We stay focused no matter what happens, and challenge ourselves to always go the extra mile and reach our goals. We are tenacious problem solvers.
- Personal Growth - Every day, we strive to become better versions of ourselves. In every challenge, we see an opportunity to be faster, smarter, stronger. We are introspective. We are energised by a love of learning new things and applying ourselves to new situations and new challenges. We are restlessly curious and ask ‘why?’ and ‘so what?’ constantly. We’re relentless self-improvers.
- Fellowship - We are confident, but not arrogant. Honest, but not cynical. Hungry, but not selfish. Mutual respect and trust fuels our high-performing team. When we lean in, pull together and lift each other up, great things happen. We care about and inspire each other to be the very best we can be.
- Ambition - Exceptional results require extraordinary ambition. We tirelessly pursue our mission. We set obsessively high standards and continue to raise the bar. We never stop innovating. We respectfully challenge existing solutions when we see a new angle to an old problem. We don't believe good enough is ever good enough.
Company HQ
Holborn, London, UK
Articles
Leadership
Yiannis Kiachopoulos
(CEO)Trained as an Information Technology Consultant at Accenture before working in business development in Germany
Artur Saudabayev
(CTO)Accomplished an M.Sc in Informatics from Edinburgh before working as an Instructor of Robotics at Nazarbayev University
Diversity, Equity & Inclusion at Causaly
Dan Atkinson (VP People)
- Diversity. Equity. Inclusion. They are more than words at Causaly. It's how we work together. It's how we build teams. It's how we grow leaders. It's what we nurture and celebrate. It's what helps us innovate. It's what helps us connect with the customers and communities we serve. We are on a mission to accelerate scientific breakthroughs for ALL humankind and we are proud to be an equal opportunity employer. We welcome applications from all backgrounds and fairly consider qualified candidates without regard to race, ethnic or national origin, gender, gender identity or expression, sexual orientation, disability, neurodiversity, genetics, age, religion or belief, marital/civil partnership status, domestic/family status, veteran status or any other difference.
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