> Markdown version of [/jobs/ext/2722888-technical-mitigations-lead](https://www.wearedevelopers.com/jobs/ext/2722888-technical-mitigations-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Mitigations Lead - **Company:** Lila Sciences, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $224,000.0 - $336,000.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Systems Theories, Monitoring of Systems, Language Modeling, Software Safety, Deployment Automation, Machine Learning Operations - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/staff-principal-research-engineer-ai-safety-technical-mitigations-lila-sciences-7906851 ## About the Role * Track record of building safety systems, classifiers, or conducting post-training for frontier-class problems - science, reasoning, programming, etc. * 4-6+ years working in technically engineering with ML systems. * Experience building scalable, production systems, not just prototypes. * Demonstrated ability to set research directions for open problems in post-training, classifier buildouts, and other relevant systems. * Ability to communicate complex technical concepts and concerns to non-expert audiences effectively. Bonus Points For * Experience in developing or applying ML to biological or physical sciences * Experience in building safeguards for scientific risks for frontier models / narrow scientific tools. * Demonstrated ability to lead teams towards engineering goals ## Description We're building a talent-dense, high-agency AI safety team at Lila that will engage all core teams within the organization (science, model training, lab integration, etc.), to prepare for risks from scientific superintelligence. The initial focus of this team will be to build and implement a bespoke safety strategy for Lila, tailored to its specific goals and deployment strategies. This will involve technical safety strategy development, broader ecosystem engagement, safety-focused evaluations, safety systems to mitigate risks, and a safety research agenda that explores longer-term needs such as oversight of superintelligent scientific systems. We're seeking a Technical Mitigations Lead, to lead the build out of safety systems at Lila for the safe deployment of our scientific capabilities to the world. Given the novelty of Lila's workflows, integrating frontier-class language models with narrow scientific tools and lab-based automation, this role will require the design and deployment of technical safeguards beyond the current state-of-the-art. We expect the person in this role to start off the initial mitigations build-out, and then slowly build a team to support this function. What You'll Be Building * Set the build and research strategy for Lila's safety systems, across scientific data analysis and generation pipelines, safety post-training, refusal classifiers, automated safety-testing / red-teaming systems, and monitoring systems. * Conduct initial safeguards experimentation and buildout for Lila's specific scientific needs, and subsequently lead a small team to execute on the build and research agenda * Lead safety systems research to iterate Lila's systems beyond the state of the art, given the needs of technical safeguards for both in silico and lab-based scientific workflows. * Partner closely with + Other members of the safety team, such as domain-specific experts (bio, chem, materials) and eval buildout teams, and + Non-safety teams, such as core AI, lab automation, and product teams, * Contributing to broader, high-quality research efforts - as and when needed - for scientific capability evaluation and restriction. * Contribute to external communications on Lila's safety efforts. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Psychological Safety in Software Engineering - Jenny-Margrethe Vej & Alexandra Hou Aldershaab](https://www.wearedevelopers.com/videos/2142-psychological-safety-in-software-engineering-jenny-margrethe-vej-alexandra-hou-aldershaab) - [Strong decisions - decisive distinctions between digital and analog aspects](https://www.wearedevelopers.com/videos/1687-strong-decisions-decisive-distinctions-between-digital-and-analog-aspects) - [Beyond the Hype: Building Trustworthy and Reliable LLM Applications with Guardrails](https://www.wearedevelopers.com/videos/1594-beyond-the-hype-building-trustworthy-and-reliable-llm-applications-with-guardrails) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)