Senior Machine Learning Engineer

Company Snapshot An early-stage AI company building a next-generation personal assistant designed to handle everyday tasks — communication, scheduling, organizing, follow-ups — with little to no user input required. The core engineering challenge is reliability: getting AI systems to execute multi-step, long-running workflows consistently, even when the underlying models behave unpredictably. The product aims to meaningfully cut down the time people spend on daily admin and coordination. The Role A senior, individual-contributor ML engineering position with full ownership of key production ML systems. This person will take vague, open-ended problems and turn them into working, scalable solutions — not a research-only role, but one grounded in shipping and maintaining live systems. What You'll Do Design and build the ML infrastructure behind a long-running, proactive AI product Own the full lifecycle — data, training, evaluation, inference, deployment, and ongoing tuning Convert experimental/research concepts into dependable production systems Diagnose and fix model and pipeline issues using live production data Work in fast iteration cycles — release, measure, adjust, repeat Partner closely with research, product, and engineering counterparts Provide technical mentorship and code/design review to other ML engineers Balance competing constraints: latency, infrastructure cost, reliability, and safety Stack Python, PyTorch/JAX, GPU-based training and inference infrastructure What We're Looking For Track record of shipping ML systems that real users depend on Strong intuition for how ML models fail in the real world, not just in theory Systems-level thinking, not just scripting — clean, production-grade code High autonomy — comfortable owning problems without close direction Fast learner, clear communicator, iterates well on feedback Success Looks Like Production ML systems hitting targets for accuracy, latency, cost, and reliability Fast diagnosis and resolution of production issues, minimal user-facing disruption Pipelines (training/inference/data) that scale and hold up over time Visible, measurable improvements driven by real usage data Peers leveling up through your review and mentorship ML work integrating smoothly into the broader product Team Culture Small, high-caliber team, flat decision-making, fast pace. Expect autonomy and structure to coexist — you're trusted to self-direct, but expected to bring rigor. ST Reg No. R1768414 BeathChapman Pte Ltd Licence no. 16S8112