Senior Machine Learning Engineer

Company SnapshotAn 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 RoleA 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 DoDesign and build the ML infrastructure behind a long-running, proactive AI productOwn the full lifecycle — data, training, evaluation, inference, deployment, and ongoing tuningConvert experimental/research concepts into dependable production systemsDiagnose and fix model and pipeline issues using live production dataWork in fast iteration cycles — release, measure, adjust, repeatPartner closely with research, product, and engineering counterpartsProvide technical mentorship and code/design review to other ML engineersBalance competing constraints: latency, infrastructure cost, reliability, and safetyStackPython, PyTorch/JAX, GPU-based training and inference infrastructureWhat We're Looking ForTrack record of shipping ML systems that real users depend onStrong intuition for how ML models fail in the real world, not just in theorySystems-level thinking, not just scripting — clean, production-grade codeHigh autonomy — comfortable owning problems without close directionFast learner, clear communicator, iterates well on feedbackSuccess Looks LikeProduction ML systems hitting targets for accuracy, latency, cost, and reliabilityFast diagnosis and resolution of production issues, minimal user-facing disruptionPipelines (training/inference/data) that scale and hold up over timeVisible, measurable improvements driven by real usage dataPeers leveling up through your review and mentorshipML work integrating smoothly into the broader productTeam CultureSmall, 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.STReg No. R1768414BeathChapman Pte LtdLicence no. 16S8112