TL;DR
AI, DeepTech and quantum executive search covers the people who build frontier technology: applied scientists and research leaders, machine learning and agentic AI engineers, platform and infrastructure engineers for large-scale compute, quantum hardware and software specialists, and robotics and advanced computing teams.
These searches sit close to our quantitative practice, because the assessment problem is the same. The scarce person is not the one who can describe a model, it is the one who can make it work reliably against real constraints, and prove that it did.
Key points
- The market rewards demonstrable systems work, not model familiarity.
- Agentic AI has created new roles in tooling, evaluation and governance that barely existed two years ago.
- Quantum and deep technology hiring is research-led, so academic networks matter as much as industry ones.
- Founding technical hires set the standard for everyone who follows, which makes the first two appointments decisive.
What we search
| Area | Roles |
|---|---|
| Applied AI and machine learning | ML engineers, applied scientists, research engineers, heads of AI |
| Agentic AI | Agent tooling and evaluation engineers, orchestration and platform leads |
| AI infrastructure | Training and inference platform engineers, GPU and compute infrastructure, MLOps |
| Data platforms | Data engineering leadership for model and product pipelines |
| Quantum computing | Quantum algorithms researchers, control and hardware engineers, error correction specialists |
| Advanced computing and robotics | Systems, embedded, perception and control engineers |
| Leadership | CTO, chief scientist, VP engineering, head of research |
| Governance | Model risk, AI safety and responsible AI leadership |
For AI talent applied specifically to trading and investment, see AI and machine learning quant recruitment.
Why this sits alongside our trading practices
The same candidates increasingly move between quantitative finance and frontier technology. A researcher who can work with noisy data and prove a result out of sample is valuable in both. An engineer who can build reliable systems around unreliable models is scarce in both. Our assessment approach, which centres on measured work and validation discipline, transfers directly.
Background reading we publish on this: agentic AI for trading, AI agents for quantitative research and agentic AI trading careers and skills.
What we assess
| Area | What we probe |
|---|---|
| Depth in the domain | Published work, shipped systems, or measured research outcomes |
| Engineering reality | Reproducibility, evaluation, cost control, reliability under load |
| Judgement | Knowing which approaches are mature and which are still research |
| Communication | Explaining technical limits honestly to boards and investors |
| Team building | Attracting and keeping scarce researchers and engineers |
| Commercial grounding | Connecting research effort to a product or a customer |
Where the talent sits
Research labs and universities, AI and deep technology companies, cloud and semiconductor firms, quantitative trading firms, robotics and defence technology, and Indian engineering centres of global technology businesses. In India, the strongest concentrations are in Bengaluru, Delhi NCR, Hyderabad and Pune, with returning senior talent from the US and Europe an increasingly common source for founding roles.
Common hiring mistakes
- Hiring a researcher when the problem needs an engineer, or the reverse.
- Buying breadth of model familiarity instead of evidence of systems that worked.
- Leaving evaluation and governance until after deployment.
- Underestimating compute and infrastructure hiring, which quietly determines research velocity.
- Hiring the second person before the first has set the technical standard.
Frequently asked questions
What roles does an AI and deep technology search cover?
Applied scientists and research leaders, machine learning and agentic AI engineers, AI infrastructure and data platform engineers, quantum and advanced computing specialists, and technical leadership including CTOs and heads of research.
Do you recruit for quantum computing companies?
Yes. SYTalent recruits for quantum computing companies across quantum algorithms research, control and hardware engineering, cryogenics and error correction. The pool is small, heavily research-led and concentrated in a handful of academic groups and national labs, which means these searches are mapped person by person rather than sourced. Expect longer timelines and candidates who weigh publication freedom alongside compensation.
How is AI hiring different from conventional engineering hiring?
Evidence matters more than tooling familiarity. The strongest candidates can show measured results, evaluation design and systems that stayed reliable, rather than a list of frameworks.
Do trading firms and AI companies compete for the same people?
Increasingly, yes. Researchers and engineers who handle noisy data, evaluation and large-scale systems are scarce in both markets, which is why we run the practices side by side.