ENJINE careers

Through machine learning, we aim to create trading strategies that exhibit both higher returns and lower risks compared to competing investment funds.

We initially plan on offering access to our algorithms through subadvisory relationships with financial advisors. In such relationships, our firm wouldn't directly manage advisors' monies, but we'd instead send them the analysis generated by our algorithms.

Once we gain enough subadvisory clients, we would then launch funds that manage our clients' monies directly and persuade our existing clients to switch over. We'd also market the funds to retail investors and institutional clients.

As compared to subadvisory relationships, we'd be able to charge higher fees on monies managed through funds. Operating funds, however, would also entail higher fixed costs and tighter regulatory regimes. The subadvisory phase of our business is therefore necessary to get the requisite scale to make the launch of funds more economical.