System1
Machine Learning Engineer
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Machine Learning Engineer
In the words of one of our senior clients recently…
“Market Research has changed more in the past 3 years, than in the previous 40”
About System1 System1 is The Creative Effectiveness Platform. Our target customers are the world’s largest advertisers. We predict and improve marketing effectiveness. Our advertising and idea tests measure emotion to give our customers the most accurate predictions of the business impact of creativity.
About The Role We are looking for a pragmatic and ambitious Machine Learning Engineer to join an empowered product team and help build the models that power System1’s Ad, Brand and Innovation products. You will work as a first-class member of the team alongside Product Managers, Engineers and Data Scientists to turn hard prediction problems into models that make our customers’ decisions measurably better. This is a rare opportunity to build real ML at the heart of a product used by the world’s largest advertisers, to work hands-on with modern tooling, and to grow your craft quickly in a team that ships.
Are you energized by building models that solve real customer problems, not just chasing benchmarks? Are you already comfortable in PyTorch and keen to go deeper, learning new architectures and techniques as the field moves? Are you motivated by getting models into products and seeing the impact, rather than research for its own sake?
Do you want to join us to excite and change the industry?
What will you be doing?
- Build, train and evaluate machine learning models in PyTorch to solve prediction problems across our Ad, Brand and Innovation products.
- Work with text, image and video data, applying the right approach, from classic ML through modern deep learning and LLMs.
- Prepare and explore datasets, building the features and pipelines your models need to perform reliably.
- Run rigorous experiments, measuring model quality with the right metrics and iterating quickly towards better results.
- Partner closely with Product Managers, Data Scientists and Engineers to frame problems and turn model outputs into real product value.
- Package and document your models so they can be handed cleanly to our engineering team for deployment.
- Keep your work reproducible and well-documented so others can understand and build on it.
- Stay curious about new tools and techniques, bringing what you learn back to the team.
- Contribute to a culture of good ML practice as the team and its capabilities grow around you.
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You need to be this kind of person:
- Pragmatic. You reach for the simplest approach that solves the problem, and you know when a heuristic beats a heavyweight model.
- Keen to learn and grow. You actively seek out new techniques, feedback and challenges, and you get better fast.
- Outcome-driven. You measure success by the impact your models have for customers and the business, not by model complexity.
- Rigorous. You care about sound evaluation, clean data, and results you can trust.
- Comfortable with ambiguity. You can take a loosely defined problem and shape it into something you can model.
- A natural collaborator, comfortable working with data scientists, engineers and product people at all levels.
- Curious about the problem domain and interested in how advertising and brands work, or excited to become so.
- Honest about what the data and models can and can’t do, and able to communicate that clearly.
You need to have the following experience:
- To be successful in this role, you will have approximately 3–5 years of hands-on machine learning experience and be ready to operate as a first-class member of an empowered product team.
- Solid, hands-on experience building and training machine learning models with PyTorch.
- A good grounding in ML fundamentals, from data preparation and feature engineering through to model evaluation.
- Strong programming skills in Python and comfort with the modern ML and data stack (e.g. NumPy, pandas, scikit-learn).
- Experience working with at least one data type in depth, whether text, images, video or structured/tabular data.
- Familiarity with deep learning approaches and an appetite to learn more, including modern architectures and LLMs.
- A track record of taking models beyond notebooks into work that others can build on and deploy.
- Good habits around experiment tracking, version control (Git) and reproducibility.
- Experience with NLP, computer vision or multimodal models is a strong nice-to-have.
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This role will require you to balance speed with the rigour our customers expect, moving quickly from an idea to a well-evaluated model.
Benefits
- We offer a competitive salary and bonus scheme, including health insurance, and pension. Our people are important to us, and we understand that each person has a life outside of work and we therefore offer flexible working arrangements (Hybrid) aiming to suit your needs. We also offer several wellbeing resources such as “de-clutter” afternoons for your personal and professional needs.
- We encourage learning and offer education reimbursement opportunities.
- We have recognition schemes, and regular socials, including summer and winter parties!
We welcome applicants from all diverse communities and encourage our candidates to ask us about reasonable adjustments that we may be able to make to support through our recruitment process and while in post.
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