Staff Machine Learning Engineer, ML Infrastructure at Simplisafe · Lumi · Lumi

Qualification

Solid grounding in ML fundamentalsProficiency in Python is requiredStaff-level technical leadershipWhat Values You’ll Share

8+ years of software/ML engineering experience, with a clear track record of building and operating production ML systems at scale.
Deep expertise in cloud ML infrastructure on Kubernetes, with hands-on production experience with Ray (which powers our inference stack); experience with KServe, Triton, vLLM, Kubeflow, Argo, or similar is a strong plus.
Strong production experience on AWS (EKS, S3, IAM, networking) and with Kafka, containerized deployments, CI/CD, and infrastructure-as-code.
Demonstrated experience designing and operating high-throughput, low-latency inference systems — GPU-aware scheduling, batching, autoscaling, multi-tenancy.
Solid grounding in ML fundamentals: how models are trained, evaluated, versioned, deployed, monitored, and rolled back in production.
Proficiency in Python is required; experience with a systems language (Go, C++, Rust) for performance-sensitive components is a plus.
Staff-level technical leadership: ability to drive ambiguous, cross-cutting initiatives, align senior stakeholders, and elevate the engineers around you without formal authority.
Strong written and verbal communication — you can make complex technical tradeoffs legible to ML scientists, product, and other infra teams.

Hands-on experience with LLM serving in production (vLLM, TGI, TensorRT-LLM, SGLang) — KV cache management, continuous batching, speculative decoding, quantization for serving.
Experience building real-time video or streaming ML pipelines (Kafka, Kinesis, Flink, or similar) at scale.
Background supporting CV workloads in production — model formats, GPU/accelerator tradeoffs, video codecs.
Experience with model lifecycle tooling (MLflow, Weights Biases, model registries, drift detection, shadow deployments).
Open source contributions to the ML infrastructure ecosystem (Ray, KServe, Triton, vLLM, Kubeflow, etc.).
Experience operating in environments with strong security and compliance requirements.
The Cloud ML team owns the full surface area — infrastructure and applied research — which means your work as a Staff infra engineer directly shapes what s possible for the science. You ll have unusual leverage: the platform you build determines how fast SimpliSafe can ship intelligent features, and the features we ship directly impact whether someone s home is safer tonight than it was yesterday.
What Values You’ll Share

Staff Machine Learning Engineer, ML Infrastructure