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.

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