At a Glance
- Tasks: Join a dynamic team to develop and scale machine learning models from research to robust experiments.
- Company: Symbolica is an innovative AI lab merging mathematics with technology to create intelligent systems.
- Benefits: Enjoy competitive salary, equity options, and the chance to work in a collaborative environment.
- Why this job: Be at the forefront of AI development, making a real impact while working with passionate experts.
- Qualifications: Experience in training deep learning models, Python, and PyTorch; curiosity about cutting-edge research is essential.
- Other info: Onsite role in London; visa sponsorship available for qualified candidates.
The predicted salary is between 43200 - 72000 £ per year.
London, UK About Us Symbolica is an AI research lab pioneering the application of category theory to enable logical reasoning in machines. We’re a well-resourced, nimble team of experts on a mission to bridge the gap between theoretical mathematics and cutting-edge technologies, creating symbolic reasoning models that think like humans – precise, logical, and interpretable. While others focus on scaling data-hungry neural networks, we’re building AI that understands the structures of thought , not just patterns in data. Our approach combines rigorous research with fast-paced, results-driven execution. We’re reimagining the very foundations of intelligence while simultaneously developing product-focused machine learning models in a tight feedback loop, where research fuels application. Founded in 2022, we’ve raised over $30M from leading Silicon Valley investors, including Khosla Ventures,General Catalyst, Abstract Ventures, and Day One Ventures, to push the boundaries of applying formal mathematics and logic to machine learning. Our vision is to create AI systems that transform industries, empowering machines to solve humanity’s most complex challenges with precision and insight.JoinustoredefinethefutureofAIbyturninggroundbreakingideasintoreality. About the Role As a Machine Learning Engineer , you’ll join a tight-knit team of experts at the cutting edge of model development — working on everything from dataset curation to scaling multi-node training runs. You’ll be responsible for turning research ideas into robust experiments, running massive sweeps, and closely tracking what works at the frontier of ML. This is a role for someone who thrives in the messy middle between research and engineering — someone who can train models at scale, but also knows when to pause and question the next hyperparameter sweep. You’ll collaborate with researchers, MLOps, and evaluation engineers to accelerate the pace and quality of experimental progress. This is an onsite role based in our London office (66 City Rd). Your Focus Own and run large-scale model training pipelines — from single-GPU prototyping to multi-node scaling Curate and manage dataset mixtures, including filtering, deduplication, and combining multiple sources Reproduce and rigorously test public baselines from recent papers Run controlled experimental sweeps, optimise for performance and stability at scale Explore and apply SoTA techniques: model distillation, synthetic data generation, parameter transfer, architectural tweaks Collaborate closely with MLOps to optimise training infrastructure and monitor runs Work hand-in-hand with model evaluation engineers to align training with meaningful metrics Support researchers in implementing new ideas and turning them into high-quality experiments Contribute to technical direction on model architecture and scaling strategy About You Proficient hands-on experience training deep learning models, ideally at scale (multi-GPU, multi-node) Strong engineering skills in Python and PyTorch (or JAX); comfortable with distributed training setups Familiarity with dataset curation, versioning, and large-scale data management Hands-on experience running and analysing hyperparameter sweeps Deep curiosity about what’s happening at the research frontier — from LLM pretraining to distillation and beyond Comfort navigating research codebases and rapidly prototyping new ideas Bonus: experience with synthetic data, RLHF-style pipelines, or model scaling laws Comfortable in a fast-moving, collaborative environment with high levels of ownership and agency What We Offer Competitive salary and early-stage equity package A chance to work at the intersection of cutting-edge research and robust engineering Ownership of training systems and direct impact on our model direction Collaborate with a team that values clarity, intellectual rigour, and hands-on experimentation We are able to sponsor a Skilled Worker visa for qualified candidates applying to this position. This specific role exceeds the minimum salary threshold set by the UK government for Skilled Worker visa sponsorship. Please note that English language proficiency at B2 level or higher is required for this role. Symbolica is an equal opportunities employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, gender, age, religion, disability, or sexual orientation. Apply for this job * indicates a required field First Name * Last Name * Email * Phone Resume/CV * Enter manually Accepted file types: pdf, doc, docx, txt, rtf Enter manually Accepted file types: pdf, doc, docx, txt, rtf Are you available to work onsite at our brand-new London office? * Select… #J-18808-Ljbffr
ML Engineer – Scalable Training & Model Development New London, UK employer: Symbolica
Contact Detail:
Symbolica Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer – Scalable Training & Model Development New London, UK
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning, particularly in areas like model distillation and synthetic data generation. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Showcase your hands-on experience with Python and PyTorch by working on personal projects or contributing to open-source initiatives. This practical experience can set you apart and demonstrate your ability to apply theoretical knowledge in real-world scenarios.
✨Tip Number 3
Network with professionals in the AI and machine learning community, especially those who work at Symbolica or similar companies. Attend relevant meetups, webinars, or conferences to build connections and gain insights into the company culture and expectations.
✨Tip Number 4
Prepare to discuss your approach to problem-solving and experimentation in machine learning. Be ready to share specific examples of how you've optimised training processes or tackled challenges in previous projects, as this aligns closely with the responsibilities of the role.
We think you need these skills to ace ML Engineer – Scalable Training & Model Development New London, UK
Some tips for your application 🫡
Understand the Role: Before applying, make sure to thoroughly read the job description for the Machine Learning Engineer position. Understand the key responsibilities and required skills, such as experience with Python, PyTorch, and large-scale model training.
Tailor Your CV: Customise your CV to highlight relevant experience in deep learning, dataset curation, and any specific projects that demonstrate your ability to work at scale. Use keywords from the job description to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your understanding of Symbolica's mission. Mention specific experiences that align with their focus on bridging theoretical mathematics and practical applications in machine learning.
Showcase Your Projects: If you have worked on relevant projects, include links to your GitHub or portfolio. Highlight any experiments you've conducted, especially those involving hyperparameter sweeps or innovative model training techniques, to demonstrate your hands-on experience.
How to prepare for a job interview at Symbolica
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with deep learning models, particularly in Python and PyTorch. Highlight any projects where you've trained models at scale, as this is crucial for the role.
✨Demonstrate Your Curiosity
Express your deep curiosity about the latest research in machine learning. Be ready to discuss recent advancements, such as LLM pretraining or model distillation, and how they could apply to Symbolica's work.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to navigate the messy middle between research and engineering. Think of examples where you've had to question hyperparameter sweeps or troubleshoot training issues.
✨Emphasise Collaboration
Since the role involves working closely with researchers and MLOps, be sure to highlight your teamwork skills. Share experiences where you collaborated on projects and how you contributed to achieving common goals.