Apptronik

1 month ago

Staff MLOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Lead the architecture and development of the MLOps platform, owning dataset lifecycle, experiment tracking, model registry, and on-robot deployment pathways. This hands-on technical leadership role partners with Autonomy, Data Platform, and TeleOp to standardize pipelines from data to deployed policies, mentoring engineers while delivering scalable ML infrastructure for robotic systems.

Location: Austin

Workplace: Onsite

Job Function: Software Engineering

Key Responsibilities

•Platform Architecture & Ownership: Define subsystem interfaces, drive architecture decisions, and establish engineering standards for how datasets, experiments, and models move through Apptronik's systems.
•Cross-Team Authority: Serve as the primary technical point of contact for Autonomy, Data Platform, and TeleOp on all matters of model lifecycle and platform contracts.
•Dataset Lifecycle & Versioning: Design and operate the dataset layer end-to-end — versioning, lineage, splits, and labeling-integration handoff; ensure every trained model can be traced back to the exact data and code.
•Model Registry & Artifact Management: Build and operate a first-class model registry — versioned artifacts, metadata, evaluation results, lineage, and approval workflows; define the promotion path to deployed models.
•Evaluation & Qualification Harnesses; Serving, Packaging & Deployment to Robot: Define offline benchmarks, simulation rollouts, policy-gating harnesses, and the on-robot packaging path (ONNX, TensorRT, torch.compile) with telemetry and observability.

Key Requirements

•Deep proficiency in Python and at least one systems-level language (Go, Rust, or C++), with demonstrated ability to make and defend architectural tradeoffs in production ML platforms.
•Proven experience owning and delivering an MLOps platform end-to-end — dataset lifecycle, experiment tracking, model registry, evaluation, and serving — at a company that ships models to production.
•Expertise across the model lifecycle: dataset versioning (DVC, LakeFS, Delta, or equivalent), experiment tracking (MLflow, W&B, Determined), model registry, and policy serving.
•Strong background designing service-oriented systems on Kubernetes; comfortable with the contract between platform APIs and underlying compute infrastructure.
•Experience defining evaluation and qualification frameworks for ML models where the cost of a regression is high (robotics, safety-critical, or production-customer-facing); plus 8+ years of professional software engineering experience in ML platforms or 4+ years of direct, hands-on experience owning an MLOps platform that shipped models to production.

Education:Master's

Skills:PythonGoRustC++KubernetesAWSGCPAzureDockerGitCI/CDONNXTensorRTTorch.compileMLflowWeights & BiasesML platformsDataset versioningModel registryEvaluation frameworks

Languages:English

Tech Stack:PythonGoRustC++KubernetesAWSGCPAzureDockerGitCI/CDONNXTensorRTTorch.compileMLflowWeights & BiasesDVCLakeFSDeltaTelemetry

Company Brief

Apptronik

Develops advanced general-purpose robots and robotic limbs for industrial and commercial applications, combining hardware, actuators, and control software to enable versatile, human-scale robot solutions.

Industry: Robotics

Company Size: Medium (51 to 250 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: Austin, United States

Founded: 2018

Website LinkedIn

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Apptronik

1 month ago

Staff MLOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Location: Austin

Workplace: Onsite

Job Function: Software Engineering

Key Responsibilities

•Platform Architecture & Ownership: Define subsystem interfaces, drive architecture decisions, and establish engineering standards for how datasets, experiments, and models move through Apptronik's systems.
•Cross-Team Authority: Serve as the primary technical point of contact for Autonomy, Data Platform, and TeleOp on all matters of model lifecycle and platform contracts.
•Dataset Lifecycle & Versioning: Design and operate the dataset layer end-to-end — versioning, lineage, splits, and labeling-integration handoff; ensure every trained model can be traced back to the exact data and code.
•Model Registry & Artifact Management: Build and operate a first-class model registry — versioned artifacts, metadata, evaluation results, lineage, and approval workflows; define the promotion path to deployed models.
•Evaluation & Qualification Harnesses; Serving, Packaging & Deployment to Robot: Define offline benchmarks, simulation rollouts, policy-gating harnesses, and the on-robot packaging path (ONNX, TensorRT, torch.compile) with telemetry and observability.

Key Requirements

•Deep proficiency in Python and at least one systems-level language (Go, Rust, or C++), with demonstrated ability to make and defend architectural tradeoffs in production ML platforms.
•Proven experience owning and delivering an MLOps platform end-to-end — dataset lifecycle, experiment tracking, model registry, evaluation, and serving — at a company that ships models to production.
•Expertise across the model lifecycle: dataset versioning (DVC, LakeFS, Delta, or equivalent), experiment tracking (MLflow, W&B, Determined), model registry, and policy serving.
•Strong background designing service-oriented systems on Kubernetes; comfortable with the contract between platform APIs and underlying compute infrastructure.
•Experience defining evaluation and qualification frameworks for ML models where the cost of a regression is high (robotics, safety-critical, or production-customer-facing); plus 8+ years of professional software engineering experience in ML platforms or 4+ years of direct, hands-on experience owning an MLOps platform that shipped models to production.

Education:Master's

Skills:PythonGoRustC++KubernetesAWSGCPAzureDockerGitCI/CDONNXTensorRTTorch.compileMLflowWeights & BiasesML platformsDataset versioningModel registryEvaluation frameworks

Languages:English

Tech Stack:PythonGoRustC++KubernetesAWSGCPAzureDockerGitCI/CDONNXTensorRTTorch.compileMLflowWeights & BiasesDVCLakeFSDeltaTelemetry

Company Brief

Apptronik

Industry: Robotics

Company Size: Medium (51 to 250 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: Austin, United States

Founded: 2018

Website LinkedIn