Staff MLOps Engineer Onsite - Austin, TX

Apptronik Systems, Inc.; Austin (TX); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Collaborative work environment

Professional development opportunities

Job summary

Apptronik Systems, Inc. is looking for a Staff MLOps Engineer to lead the technical direction of their MLOps platform, focusing on datasets, experiments, and model artifacts. This role involves defining architecture, ensuring model lifecycle compatibility, and leading cross-functional teams.

The ideal candidate will have a Master’s degree in Computer Science or a related field and at least 8 years of experience in using machine learning platforms.

Benefits include a collaborative work environment and opportunities for professional growth.

Qualifications

• Proven experience in end-to-end MLOps platform delivery.
• 8+ years in software engineering or 4+ in MLOps.
• Familiarity with humanoid robotics or teleoperation.

Responsibilities

• Own platform architecture and establish engineering standards.
• Serve as the technical point of contact across teams.
• Mentor junior engineers and influence platform standards.

Skills

Proficiency in Python

Experience with systems-level languages (Go, Rust, C++)

MLOps platform engineering

Service-oriented systems on Kubernetes

Cloud infrastructure (AWS, GCP, Azure)

Docker and CI/CD workflows

Education

Master’s degree in Computer Science or related field

Bachelor’s degree with exceptional experience

Tools

DVC

MLflow

ONNX Runtime

Job description

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of embodied AI, applying our expertise across the full robotics stack to solve some of society's most important problems.

Job Summary

Apptronik is seeking a Staff MLOps Engineer to own the technical direction of our MLOps platform that serves as the system of record for datasets, experiments, model artifacts, and serving paths connecting teleoperation data collection to deployed autonomy on Apollo. In this role you will define the architecture for the platform layer above the training cluster, set standards, and partner closely with engineers across MLOps, Autonomy, Data Platform, and TeleOp.

Responsibilities and Duties

• Technical Direction: Own the platform architecture, define subsystem interfaces, and establish engineering standards for data and model flow.
• Cross‑Team Authority: Serve as primary technical point of contact for Autonomy, Data Platform, and TeleOp on all model lifecycle matters.
• Dataset Lifecycle & Versioning
  • Design and operate end‑to‑end dataset layer, including versioning, lineage, splits, and labeling integration.
  • Ensure reproducibility by tracing models back to exact data and code.
  • Build and operate a first‑class model registry with versioning, metadata, evaluation, lineage, and approval workflows.
  • Define promotion path from “trained” to “qualified” to “deployed to robot.”
  • Set up automated evaluation through benchmarks, simulation rollouts, and policy‑gating harnesses.
  • Develop a metrics framework that gates releases.
• Serving, Packaging & Deployment to Robot
  • Own the path from registered model to running inference on Apollo, including packaging (ONNX, TensorRT, torch.compile), versioning on‑robot, rollback, and observability.
  • Coordinate with Connect and Data Platform on deploy‑and‑telemetry integration.
• Mentorship & Cross‑Functional Leadership
  • Mentor mid‑level and senior engineers through code review, design review, and collaboration.
  • Influence research workflows and standardize platform primitives across teams.

Skills and Requirements

• Deep proficiency in Python and a systems‑level language (Go, Rust, or C++).
• Proven experience owning and delivering an end‑to‑end MLOps platform that shipped models to production.
• Expertise across the model lifecycle: dataset versioning (DVC, LakeFS, Delta, etc.), experiment tracking (MLflow, W&B, Determined), model registry, and policy serving.
• Strong background designing service‑oriented systems on Kubernetes and understanding platform‑API contracts.
• Experience defining evaluation and qualification frameworks for high‑cost ML models.
• Experience leading technical projects end‑to‑end: architecture, implementation, validation, and iteration.
• Proficiency with cloud infrastructure (AWS, GCP, or Azure), Docker, Git, and CI/CD workflows.
• Eligibility under U.S. export‑control regulations to access technical data and software.

Education and Experience

• Master’s degree in Computer Science, Machine Learning, or related technical field preferred; Bachelor’s considered with exceptional experience.
• 8+ years of professional software engineering experience in ML platforms or related infrastructure, or 4+ years of direct ownership of an MLOps platform that shipped models to production.

Preferred Qualifications

• Experience deploying ML models to edge or embedded targets (ONNX Runtime, TensorRT, robot fleets).
• Experience with RL training and evaluation infrastructure for embodied agents.
• Familiarity with humanoid robotics, dexterous manipulation, or teleoperation data domains.
• Experience with simulation‑in‑the‑loop evaluation (IsaacSim, MuJoCo, etc.).
• Familiarity with policy gating, shadow deployments, or staged rollout strategies for autonomy.
• Open‑source contributions to MLOps platform tooling (MLflow, BentoML, KServe, Ray Serve, etc.).

Physical Requirements

• Prolonged periods of sitting at a desk and working on a computer.
• Must be able to lift 15 pounds at times.
• Vision to read printed materials and a computer screen.
• Hearing and speech to communicate.

Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.