Founding ML Engineer | Personalised Cancer Vaccines
—
I’m working with a well-backed, early-stage company in London building personalised cancer vaccines. They treat this as a deep technical challenge first: modelling the evolutionary arms race between tumours and the immune system, where signals are sparse, ground truth is costly, and predictions need to be trustworthy in the clinic. Small, driven team + real patient projects and data from day one - no waiting around. They’re hiring a Founding ML Engineer to own and build the core ML platform that designs these vaccines. The problem space is unusually interesting: • Sequence models + structured prediction for neoantigen targeting • Evolutionary dynamics, selection pressures, inference from limited/noisy observations • Game-theoretic thinking about tumour-immune interactions You’ll go from research ideas → production systems: standing up training infra, leveraging serious compute, prioritising clean abstractions. They want someone who: • Has strong mathematical foundations (reads methods sections in top papers comfortably) • Brings a genuine publication record in strong venues (NeurIPS, ICLR, ICML etc.) • Can independently build foundational models - mix of fine-tuning SOTA architectures + designing from scratch • Is passionate about hard, high-stakes prediction problems (sceptical of pure leaderboard chasing) • Can ship fast without regrettable tech debt Biological domain knowledge helpful but not required - they’ll teach the oncology context. Why this role stands out • Backed by genuine industry leaders + top US VCs • Strong initial funding and long runway • Live customer projects - impact from week one • Large R&D budget + fast experimental validation loops for models • Flexible start dates (finishing PhD later in 2026? No issue) Hands-on founding role with real ownership. London-based. Salary: £100,000 - £200,000+ meaningful equity (they pay for impact). Central London (On-site)
| Skill | Source | Confidence |
|---|---|---|
| Neural Networks | llm_hard |
100%
|
| Deep Learning | llm_hard |
100%
|
| Transformers | llm_hard |
100%
|
| Fine-tuning Models | llm_hard |
100%
|
| Model Deployment | llm_hard |
80%
|
| Model Optimization | llm_hard |
80%
|
| Skill | Source | Confidence |
|---|---|---|
| Problem-Solving | llm_soft |
100%
|
| Critical Thinking | llm_soft |
100%
|
| Analytical Thinking | llm_soft |
100%
|
| Working Independently | llm_soft |
100%
|
| Decision-Making | llm_soft |
80%
|
| Self-Management | llm_soft |
80%
|
| Query | Country | Status | Response ms | Created |
|---|---|---|---|---|
| Founding ML Engineer | Personalised Cancer Vaccines | extracted | 4236 | 2026-03-22 02:11 | |
| Founding ML Engineer | Personalised Cancer Vaccines | classified | 580 | 2026-03-21 20:56 | |
| junior deep learning engineer in United Kingdom | gb | duplicate | 13733 | 2026-03-21 17:11 |
| junior ML engineer in United Kingdom | gb | duplicate | 22049 | 2026-03-21 17:00 |
| junior machine learning engineer in United Kingdom | gb | duplicate | 9050 | 2026-03-21 16:57 |
| junior data scientist in United Kingdom | gb | processed | 15536 | 2026-03-21 16:54 |
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"job_description": "I’m working with a well-backed, early-stage company in London building personalised cancer vaccines.\n\nThey treat this as a deep technical challenge first: modelling the evolutionary arms race between tumours and the immune system, where signals are sparse, ground truth is costly, and predictions need to be trustworthy in the clinic.\n\nSmall, driven team + real patient projects and data from day one - no waiting around.\n\nThey’re hiring a Founding ML Engineer to own and build the core ML platform that designs these vaccines.\n\nThe problem space is unusually interesting:\n• Sequence models + structured prediction for neoantigen targeting\n• Evolutionary dynamics, selection pressures, inference from limited/noisy observations\n• Game-theoretic thinking about tumour-immune interactions\n\nYou’ll go from research ideas → production systems: standing up training infra, leveraging serious compute, prioritising clean abstractions.\n\nThey want someone who:\n• Has strong mathematical foundations (reads methods sections in top papers comfortably)\n• Brings a genuine publication record in strong venues (NeurIPS, ICLR, ICML etc.)\n• Can independently build foundational models - mix of fine-tuning SOTA architectures + designing from scratch\n• Is passionate about hard, high-stakes prediction problems (sceptical of pure leaderboard chasing)\n• Can ship fast without regrettable tech debt\n\nBiological domain knowledge helpful but not required - they’ll teach the oncology context.\n\nWhy this role stands out\n• Backed by genuine industry leaders + top US VCs\n• Strong initial funding and long runway\n• Live customer projects - impact from week one\n• Large R&D budget + fast experimental validation loops for models\n• Flexible start dates (finishing PhD later in 2026? No issue)\n\nHands-on founding role with real ownership. London-based.\n\nSalary: £100,000 - £200,000+ meaningful equity (they pay for impact).\n\nCentral London (On-site)",
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"employer_website": "https://www.cubiqrecruitment.com",
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