Senior Machine Learning Researcher

Our client is an established proprietary trading firm trading its own capital across global markets. No external investors, no clients; research goes straight into production, and performance is the only feedback loop that matters.

• They are building out a deep learning research function and are actively interviewing now. We're partnering with them confidentially on a small number of senior hires. Firm details shared on a call.
• The role

This is a frontier-scale deep learning research seat, not an applied-LLM or platform role. You'll be doing the modelling work itself, architecture, objectives, training dynamics, on models at the largest scale, with the compute and data to match.

• What you'll do
• Research and design deep learning models and architectures at scale
• Work on pre-training: architecture design, objectives and loss design, data mixture as a modelling decision, optimization and training stability, scaling behavior
• Take ideas from hypothesis through large-scale training runs to production
• Push on the parts of the stack that actually move model quality — attention variants, long context, mixture-of-experts routing, tokenization, curriculum
• Work alongside researchers, traders and infrastructure engineers who handle the plumbing so you can stay on the modelling

• What they're looking for
• 5+ years of LLM research experience
• Experience at genuine scale. You've worked on frontier-class models. Think the largest publicly known model generations and above, and can talk concretely about parameter counts, token budgets, cluster size and what broke.
• A pre-training background is the strongest fit. Post-training profiles are welcome where the work is heavily modelling RL algorithm design, reward modelling, objective design, distillation research — rather than data pipeline ownership or infrastructure
• Deep fluency in PyTorch (or JAX) and genuine command of the theory, not API-level familiarity
• Strong publication record is a plus, not a requirement — frontier-run experience speaks for itself

Trading experience is not required and not expected. They hire from big tech, frontier AI labs, and research groups doing serious work at scale.

• Why it's worth a conversation
• A genuine research seat with serious compute — no product roadmap, no launch cycle, no committee between your idea and a training run
• Immediate, measurable feedback on every model you ship
• Small team, high trust, no organizational drag
• Compensation for this skillset is genuinely open-ended at the top end — well beyond standard big-tech senior bands