Applied research engineer Work, Jobs (with Salaries)

Applied Research Engineer - Post-Training
Helical —London
- Full-time
- Doctoral degree
- Master's degree
- Machine learning
Quick apply
Research Engineer, Machine Learning (Reinforcement Learning)
Anthropic —London
- £260,000 - £630,000 a year
- Hybrid work
- System design
- Master's degree
- C++
- Flexible schedule
Graduate Applied Research Engineer
Xapien —London
- £30,000 - £40,000 a year
- Full-time
- Hybrid work
- AWS
- Communication skills
- Python
- Life insurance
Quant Research Analyst
Revolut —Remote
- Remote
Applied AI / LLM Engineer (Python)
Mad Devs —Remote
- Remote
- English
- Master's degree
- SQL
- Paid training
- Flexible schedule
9d
Senior Research Engineer
Decagon —London
- £200,000 - £300,000 a year
- Full-time
- AI
- Python
- Life insurance
Founding Product Engineer - Applied AI
Healthtech-1 —London
- £90,000 - £130,000 a year
- Overtime
- APIs
- AI
- School
- Annual leave
- Work from home
Applied Research - Artificial Intelligence - London - Associate
Goldman Sachs —London
- Full-time
- Doctoral degree
- Computer Science
- Master's degree
- Employee assistance programme
AI Research Engineer
Alpine —Enstone
- Full-time
- Azure
- Doctoral degree
- Master's degree
Research Scientist
Leonardo UK Ltd —Luton
- £45,000 - £58,000 a year
- Full-time
- Hybrid work
- Doctoral degree
- Maths
- Flexible schedule
- Referral programme
Machine Learning Research Engineer
Cambridge University Press & Assessment —Cambridge
- £49,170 - £63,915 a year
- Hybrid work
- Doctoral degree
- Data analysis skills
- English
- Annual leave
10d
Applied AI Engineer
G-Research —London
- C++
- AI
- Python
- Annual leave
Principal Research Engineer
Synthesia —London
- Full-time
- Remote
- Communication skills
AI Platform Engineer (EU/UK Based - Remote)
Duvo Inc —London
- £110,000 - £250,000 a year
- Full-time
- Remote
- System design
- APIs
- AI
Senior Product Engineer
Ankar —London
- £160,000 - £300,000 a year
- Full-time
- Hybrid work
- Travel
- No travel
AI Engineer - Platform & Assistant
Build Technologies —London
- £125,000 - £225,000 a year
- Full-time
- Master's degree
- APIs
- AI
Quick apply
Product Engineer
Ankar —London
- £96,000 - £160,000 a year
- Full-time
- Travel
- No travel
Applied AI Architect, Industries
Anthropic —London
- £150,000 - £190,000 a year
- Hybrid work
- Sales
- Master's degree
- Bachelor's degree
- Flexible schedule
Research and Development Scientist
Eluceda Limited —Burnley
- £25,000 - £32,000 a year
- Full-time
- Microsoft Powerpoint
- Microsoft Word
- Microsoft Excel
- Free parking
- Annual leave
Quick apply
1d
Product Designer
Geordie AI —London
- Full-time
- Hybrid work
- AI
- Communication skills
Quick apply
14d

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Applied Research Engineer - Post-Training

Helical -
London

Quick apply

Job details

Full-time

Qualifications

Node.js
Doctoral degree
PyTorch
Master's degree
Machine learning
Communication skills
Python

Full job description

Helical is building the in-silico labs for biology

Drug discovery still relies on wet labs: slow, expensive, and constrained by physical trial-and-error. Helical is changing that.

We build the application layer that makes Bio Foundation Models usable in real-world drug discovery, enabling pharma and biotech teams to run millions of virtual experiments in days, not years. Today, leading global pharma companies already use Helical, and we’re at the start of a highly ambitious growth journey.

We’re a founder-led, talent-dense team building a category-defining company from Europe. We care deeply about the quality of our work, move fast, and expect ownership. If you’re excited by complexity, real responsibility, and shaping how a company actually operates as it scales, you’ll feel at home here.

At Helical, we’re focused on leveraging research to transform the future of drug discovery. We are seeking an Applied Research Engineer - Post-Training to join our team, focusing on maximizing the performance of cutting-edge foundation models in real-world applications.

Your Role

You will own the full post-training lifecycle for biological foundation models—from alignment strategy to production deployment. This means designing and running pipelines that transform general-purpose models into therapeutic-specific tools for our pharma clients. You'll work directly with real drug discovery problems: adapting models to disease areas, cell types, and perturbation contexts that matter for target identification, hit discovery, and beyond.

This isn't a support role. You'll make core technical decisions about how we extract value from foundation models—what to fine-tune, how to validate it biologically, and how to ship it to customers who are running experiments that inform real clinical programs. You'll collaborate closely with our ML infrastructure and biology teams, but you'll be the person responsible for whether our post-training actually works.

What You'll Do

Design and implement post-training pipelines that align biological foundation models to specific therapeutic contexts and client use cases.
Build validation frameworks that connect model improvements to biological ground truth—working with embeddings, perturbation data, and external resources like OpenTargets.
Own experiments end-to-end: from hypothesis through training runs on distributed GPU infrastructure to analysis and client delivery.
Collaborate with ML engineers on training infrastructure and with biologists on ensuring outputs are scientifically meaningful.
Contribute to our open-source tooling (helical-package) and help shape the technical direction of our post-training capabilities as we scale.
Stay at the frontier of post-training research and bring relevant advances into production.

Requirements

Essentials

MSc or PhD in Machine Learning, Computational Biology, or a related field—or equivalent depth gained through industry experience.
Hands-on experience with post-training techniques: fine-tuning, LoRA, DPO, RLHF, or similar alignment methods.
Strong proficiency in Python and PyTorch. You should be comfortable writing training loops, debugging distributed runs, and working directly with model internals.
Familiarity with transformer architectures and how they behave in practice—not just theory.
Experience designing and running experiments rigorously: tracking metrics, iterating systematically, and drawing valid conclusions from results.
Ability to work autonomously and make decisions with incomplete information. We're a small team; you'll own problems end-to-end.
Clear communication skills—you'll need to explain technical trade-offs to colleagues across ML, biology, and product.

Bonus Points

Experience with biological foundation models (Geneformer, scGPT, ESM, or similar) or computational biology more broadly.
Familiarity with drug discovery workflows, target identification, or perturbation biology.
Track record of shipping post-training improvements into production systems.
Experience with distributed training infrastructure (multi-GPU, multi-node, NCCL, DeepSpeed, FSDP).
Publications at ML or computational biology venues (NeurIPS, ICML, ICLR, Nature Methods, etc.).
Contributions to open-source ML tooling.

Quick apply

Refine Your Search

applied research engineer jobs

Applied Research Engineer - Post-Training

Research Engineer, Machine Learning (Reinforcement Learning)

Graduate Applied Research Engineer

Quant Research Analyst

Applied AI / LLM Engineer (Python)

Senior Research Engineer

Founding Product Engineer - Applied AI

Applied Research - Artificial Intelligence - London - Associate

AI Research Engineer

Research Scientist

Machine Learning Research Engineer

Applied AI Engineer

Principal Research Engineer

AI Platform Engineer (EU/UK Based - Remote)

Senior Product Engineer

AI Engineer - Platform & Assistant

Product Engineer

Applied AI Architect, Industries

Research and Development Scientist

Product Designer

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