Research Engineer - Machine Learning
SKILLS
FULL DESCRIPTION
Research Engineer - Machine Learning
Type: Full-time
Location: Remote (UK/EU based)
Compensation: Competitive (plus equity commensurate with experience)
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## About us
[Employer hidden — view at passion-project.co.uk] is pioneering sustainable agriculture through AI-powered molecular glue discovery. Backed by Speedinvest and Nucleus Capital, we are building a computational platform to bring targeted protein degradation to agriculture.
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## The role
We are looking for an experienced Research Engineer to join our engineering team and help integrate generative AI models into [Employer hidden]’s molecular glue discovery and design platform. You will work alongside a team of ML scientists and engineers, implementing research prototypes into robust, scalable systems.
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## Key responsibilities
- Implement and productionise ML models by translating research prototypes into robust, maintainable, and well-tested codebases.
- Design, build, and maintain infrastructure for data ingestion, preprocessing, training, inference, and evaluation.
- Optimise and scale distributed training and inference pipelines across GPUs, clusters, and cloud environments.
- Add monitoring, logging, and experiment-tracking to models and systems using tools such as Weights & Biases and MLflow.
- Collaborate with research scientists to accelerate experiments, validate results, and ensure reproducibility.
- Contribute to engineering standards, perform code reviews, share best practices, and support a culture of technical excellence.
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## What you will bring
- PhD or MSc in Computer Science, (Applied) Mathematics, Statistics, or a related technical field.
- 2+ years of experience in fast-paced research or engineering environments, ideally as an early-stage ML or software engineer in a startup.
- Proven expertise in building and managing ML infrastructure for large-scale training, inference, and deployment.
- Experience navigating and extending complex research codebases, including open-source frameworks and academic implementations.
- Proficiency in PyTorch and MLOps / DevOps tooling (Weights & Biases, Docker, Kubernetes), with experience in CI/CD and cloud infrastructure (GCP, AWS, or SLURM-based HPC).
- Strong background in software engineering best practices, including testing, monitoring, versioning, and documentation.
- Excellent communication and documentation skills, with a strong bias for reproducibility and collaboration.
- A proactive, delivery-oriented mindset and a passion for enabling cutting-edge research through scalable systems.
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## Nice to have
- Experience building or extending infrastructure for large-scale training, distributed optimisation, or model evaluation pipelines.
- Familiarity with experiment-tracking and monitoring frameworks (Weights & Biases, MLflow) and MLOps/DevOps tooling (Docker, Kubernetes, Terraform).
- Knowledge of bioinformatics or molecular simulation software stacks (RDKit, OpenMM, GROMACS, PyRosetta).
- Exposure to infrastructure-as-code, cloud orchestration, and GPU cluster management.
- Interest in applied AI for science, and a desire to collaborate closely with researchers to turn prototypes into production-ready systems.
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## Why join us
Competitive salary and meaningful equity, fully remote work, support for conference attendance, publications, and patents. Be part of a founding team shaping AI-driven agriculture and contribute to global food security.
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## Application process
- CV review — We look for relevant expertise, strong motivation, and alignment with our mission.
- First interview (exploratory) — Informal conversation with a founding team member to discuss background and interests.
- Second interview (technical) — Technical interview with engineering and research team on algorithm design and experimental validation.
- References and offer — References checked, then offer extended if aligned.