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- Tasks: Lead the development of autonomous systems for complex scientific tasks through natural language.
- Company: Join Latent Labs, pioneers in synthetic biology and generative AI.
- Benefits: Enjoy private health insurance, generous leave, hybrid work, and pension contributions.
- Why this job: Make a real impact in science with cutting-edge technology and innovative workflows.
- Qualifications: Strong software engineering skills in Python and experience with LLM orchestration.
- Other info: Collaborate with top minds in the field and explore exciting career growth opportunities.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Member of Technical Staff – Reasoning Workflows
Latent Labs
We are looking for a highly skilled Member of Technical Staff to lead the development of cutting‑edge workflows. You will build autonomous systems that can navigate complex scientific tasks entirely through natural language conversation. In your role you will architect and deploy reasoning systems that democratise access to breakthrough synthetic biology tools, enabling researchers worldwide to leverage our frontier models through intuitive chat interfaces.
Who we are
At Latent Labs, we are building frontier models that learn the fundamentals of biology. We pursue ambitious goals with curiosity and are committed to scientific excellence. Our team co‑developed DeepMind’s Nobel‑prize winning AlphaFold, invented latent diffusion, and built pioneering lab data management systems as well as high‑throughput protein screening platforms. We work with some of the brightest minds in generative AI and biology.
Who You Are
- You are a strong software engineer with deep experience in Python, API design, and distributed systems architecture.
- You are an expert in LLM orchestration. You have hands‑on experience with LLM APIs (OpenAI, Anthropic, etc.) and orchestration frameworks like LangChain, LlamaIndex, or have built custom agent frameworks from scratch.
- You understand intelligent information retrieval. You have experience with RAG (Retrieval‑Augmented Generation) systems, vector databases, and embedding models for knowledge extraction.
- You can architect complex workflows. You have experience with workflow orchestration tools (Airflow, Prefect, Temporal) or have built custom pipeline systems for multi‑step autonomous processes.
- You bridge science and engineering. You are comfortable with scientific computing libraries (NumPy, SciPy, pandas) and understand scientific literature formats, databases (PubMed, arXiv), and academic data processing.
What Sets You Apart
- You have a research background. You are a former academic researcher who transitioned to industry ML/AI roles, or a research software engineer with deep ML/AI experience.
- You’re passionate about scientific automation. You have experience with document processing, OCR, text extraction from academic papers, and scientific data formats.
- You understand the research ecosystem. You have worked in academic or pharmaceutical research environments and understand research workflows and publishing processes.
- You’re a multimodal specialist. You have a background in natural language processing, particularly for scientific text processing and citation networks.
Your Responsibilities
- Build autonomous scientific agents that can execute complex research workflows through natural language interaction—from protein structure analysis to experimental design.
- Architect end‑to‑end reasoning systems that integrate our platform capabilities with intelligent decision‑making, enabling users to accomplish sophisticated tasks through simple chat interfaces.
- Develop knowledge discovery pipelines that can autonomously mine scientific literature, identify untargeted disease pathways, and propose novel therapeutic targets.
- Create scientific content at scale by building agents that can design experiments, generate hypotheses, and produce research‑grade articles and blog posts.
- Pioneer autonomous lab workflows by developing agents that can design complex biological systems (like protein‑based logic gates) and orchestrate their validation.
- Collaborate with scientists to understand research pain points and translate them into intelligent automation solutions.
- Publish and evangelise breakthrough applications of agentic workflows in synthetic biology through articles, blog posts, and scientific demonstrations.
Benefits
- Private health insurance
- Pension/401(K) contributions
- Generous leave policies (including gender‑neutral parental leave)
- Hybrid working
- Travel opportunities and more
We welcome applicants from all backgrounds and are committed to building a team that represents a variety of backgrounds, perspectives, and skills.
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Member of Technical Staff - Reasoning Workflows employer: Latent Labs
Contact Detail:
Latent Labs Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Member of Technical Staff - Reasoning Workflows
✨Tip Number 1
Network like a pro! Reach out to folks in your field on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those related to Python and LLM orchestration. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios relevant to reasoning workflows. Mock interviews with friends can help you nail your responses and boost your confidence.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Member of Technical Staff - Reasoning Workflows
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your expertise in Python, API design, and any relevant projects you've worked on that showcase your ability to build autonomous systems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about scientific automation and how your background aligns with our mission at Latent Labs. Share specific examples of your work with LLM orchestration or workflow tools to make your application stand out.
Showcase Your Research Experience: If you have a research background, don’t hold back! Discuss your experience in academic or pharmaceutical environments and how it has shaped your understanding of research workflows. This will help us see how you can bridge science and engineering.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at Latent Labs
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills and get familiar with API design and distributed systems architecture. Be ready to discuss your hands-on experience with LLM APIs and orchestration frameworks like LangChain or LlamaIndex. They’ll want to see that you can not only talk the talk but also walk the walk!
✨Showcase Your Research Background
Since they value a research background, prepare to share specific examples of your academic work and how it translates to industry roles. Highlight any experience you have with scientific automation, document processing, or working in research environments. This will show them you understand the nuances of their field.
✨Demonstrate Problem-Solving Skills
Be ready to tackle hypothetical scenarios related to building autonomous scientific agents or designing complex workflows. Think through your approach to intelligent decision-making and how you would integrate platform capabilities. They’ll be looking for your ability to think critically and creatively.
✨Communicate Clearly and Confidently
Since the role involves translating complex scientific tasks into intuitive chat interfaces, practice explaining your ideas clearly. Use simple language to describe your past projects and how they relate to the job. Good communication is key, especially when collaborating with scientists and stakeholders.
