Senior Applied-ML Engineer

About Mandolin

Nearly all disease will become treatable in our lifetimes, and drug discovery is quickly becoming an engineering discipline. Mandolin is building the “last-mile” delivery infrastructure that gets cutting-edge biologics, cell, and gene therapies to patients faster. Our AI-powered knowledge-worker platform already serves leading infusion clinics, with payers and pharma next in line.

We’re backed by Greylock, SignalFire, Maverick, and founders of famous companies like Yahoo, and led by repeat and exited founders with a team hailing from some of the most technically impressive companies.

Why this role matters

Our copilots handle ~80 % of clinic back-office workload today. Reaching 99 % means :

capturing the long-tail edge cases that bury teams in rework,

pushing the latest foundation models to their accuracy and cost limits, and

proving every improvement with airtight, regulator-ready evaluation.

We need an applied-ML architect who has already built this bridge from “impressive demo” to “lights-out production.” You will design the core ML infrastructure that turns Mandolin from helpful copilot into true autopilot—where work closes itself and clinicians only handle the exceptions.

What you’ll own

Full ML lifecycle. Architect HIPAA-compliant pipelines—data capture → training → inference → human-in-the-loop feedback—to hardening and rollout.

Model strategy. Select or fine-tune SOTA vision, language, and multimodal models; apply LoRA, RAG, distillation, or quantization when ROI is clear.

Evaluation harnesses. Build automated, statistical tests that surface long-tail failure modes and quantify business impact before any deploy.

Performance engineering. Drive latency, throughput, and dollar-per-task down across GPU, CPU, and serverless footprints.

Product integration. Pair with forward-deployed engineers to turn field discoveries into new datasets, metrics, and rapid model iterations.

Technical leadership. Mentor a junior ML engineer and establish best-in-class tooling, conventions, and review culture for the ML function.

Must-have experience

5 + years shipping ML systems (vision, NLP, or multimodal) that serve live traffic.

Expert Python plus PyTorch or JAX; comfortable with distributed training and experiment tracking.

Track record of taking a research spike to a monitored, fault-tolerant, low-latency service.

Deep understanding of eval design, ablations, and statistical significance in noisy real-world data.

Fluency with modern foundation-model techniques : LoRA / PEFT, RAG, vector search, structured prompts, model compression.

Bias toward measurable impact and disciplined rollout in regulated or high-reliability domains.

Nice-to-haves

Healthcare, document understanding, or complex form-extraction experience.

Production work on Vertex AI, Kubeflow, or similar cloud ML stacks.

Open-source contributions, peer-reviewed research, or public technical writing.