About the Company
At Future Secure AI, we're building something genuinely new — and we're looking for people bold enough to build it with us. We work at the frontier of AI, tackling big, real-world problems for global enterprises across multiple industries, armed with state-of-the-art technology and a culture that prizes courage, rigor, and relentless curiosity. Our BRAVER values aren't just words on a wall — they describe the kind of people we are and the standard we hold ourselves to every day. Our leadership team is entrepreneurial, experienced, and accessible, with an open-door policy that means you'll never be just a number here. We invest seriously in your growth because we know our success depends on yours. If you're ready to work alongside some of the brightest minds in the industry, push into uncharted territory, and do work that genuinely matters, Future Secure AI is the place for you.
As Delivery Lead — Research, you will be the operational engine behind FSAI's applied research function. You will sit at the intersection of research and engineering, translating cutting-edge AI and machine learning work into structured, deliverable programmes that reach enterprise clients at scale.
This is a role for someone who understands how research actually works — the ambiguity, the iteration, the pivots — and who knows how to build the delivery rigour around it without killing the creative process.
Responsibilities
- Research Programme Leadership: Own the end-to-end delivery of complex, cross-functional research programmes — from problem framing and hypothesis definition through to deployment — ensuring alignment with company strategy, client commitments, and research integrity
- Roadmap and Planning: Define and maintain research delivery roadmaps in close collaboration with research scientists, engineering, and product leadership, sequencing work to balance exploratory investigation with commitments to ship
- Cross-Functional Orchestration: Connect and coordinate research scientists, ML engineers, product managers, and client delivery teams, ensuring findings translate into working systems rather than sitting in notebooks
- Risk & Dependency Management: Anticipate where research timelines are at risk — model performance, data availability, infrastructure constraints — and drive resolution before they become blockers
- Process & Efficiency: Build lightweight, research-appropriate delivery processes that bring accountability and predictability without imposing rigid structure on inherently iterative work
- Stakeholder Communication: Translate research progress, setbacks, and findings into clear, honest updates for technical and non-technical stakeholders, including at executive level
- Strategic Influence: Shape how the organisation prioritises research investment, drawing on delivery data, client feedback, and emerging capability to inform what gets built next
- Team Enablement: Give research teams the tooling, process support, and operational clarity they need to focus on the work that matters
- Research-to-Product Pipeline: Manage the critical handoff between research output and production AI systems, ensuring models and findings are integrated into the core platform in a governed, scalable way
Minimum Qualifications
- Bachelor's degree in Mathematics, Data Science, Statistics, Computer Science or a related discipline
- 10+ years of experience delivering complex technical or research programmes, with at least 3 years in an AI or ML environment
- Demonstrated ability to manage delivery in ambiguous, research-driven contexts where outputs are uncertain and timelines are estimates
- Strong grasp of software development and research methodologies — Agile, Scrum, and the ability to adapt when standard frameworks don't fit
- Exceptional communication skills, with the ability to hold a room with research scientists and C-suite stakeholders equally
- Analytical and data-driven in how you make decisions and report progress
- Experience with LLM-driven research or applied NLP is highly preferred
- Familiarity with cloud platforms (AWS, Azure, or GCP)
Preferred Qualifications
- Master's degree or Doctorate in Data Science, Mathematics, Statistics, Computer Science or a related discipline
- Hands-on familiarity with modern LLM ecosystems — LangChain, LlamaIndex, RAG pipelines, multi-agent frameworks, or GPT-based systems
- Working knowledge of NLP techniques: transformers, topic modelling, supervised NLP, LSTMs
- Python experience in a machine learning or research context
- Prior experience as a research engineer, ML scientist, or technical lead before moving into delivery
Why Join Us?
- A high-performance culture
- State-of-the-art technology
- Experience world-class leadership
- Scale of impact and purpose
- A competitive salary and a huge growth trajectory
- Work with the best in the industry
- Flexible work environment
- Diversity and creativity
Disclaimer: We do not wish to be contacted by recruitment agencies. Our hiring process is managed in-house and the best way for candidates to express interest is by applying with your resume through our company website.
At Future Secure AI, we are committed to protecting your privacy and adhering to the principles of the General Data Protection Regulation (GDPR) and the Australian Privacy Principles (APPs) under the Privacy Act 1988 (Cth). Our Privacy Policy outlines how we collect, use, share, and protect your personal data when you visit our website at www.futuresecure.ai (the "Website") and use our services.
