--- title: 'Staff MLOps Engineer at Apptronik' canonical: 'https://feeny.ai/job/staff-mlops-engineer-apptronik-austin-wqpzsp0cg14t' type: 'job' last_seen: '2026-09-08' --- # Staff MLOps Engineer at Apptronik - **Company:** Apptronik - **Location:** Austin, TX - **Work type:** onsite - **Posted:** 2026-05-29 - **Last confirmed live:** 2026-09-08 - **Apply:** https://boards.greenhouse.io/apptronik/jobs/6008787004?gh_jid=6008787004 ## 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 Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better. ## JOB SUMMARY Apptronik is seeking a Staff MLOps Engineer to own the technical direction of our MLOps platform — the system of record for datasets, experiments, model artifacts, and serving paths that connects teleoperation data collection on one side to deployed autonomy on Apollo on the other. In this role, you will set the architecture for the platform layer above the training cluster: dataset lifecycle, experiment tracking, model registry, evaluation harnesses, and the serving / packaging path that delivers trained policies to robots in the field. You will lead by influence across MLOps, Autonomy, Data Platform, and TeleOp — establishing the standards, contracts, and tooling that turn one-off research code into a repeatable, auditable pipeline from data to deployed model. This is a hands-on technical leadership role, not a management position; you will be a primary contributor while mentoring the engineers around you, and partnering closely with the Training Infrastructure engineer who owns the cluster layer beneath the platform. ## ESSENTIAL DUTIES AND RESPONSIBILITIES Platform Architecture & Ownership - Technical Direction: Own the technical direction for the MLOps platform — 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 - Versioning & Lineage: Design and operate the dataset layer end-to-end — versioning, lineage, splits, and labeling-integration handoff. - Reproducibility: Ensure every trained model can be traced back to the exact data and code that produced it. Model Registry & Artifact Management - Registry: Build and operate a first-class model registry — versioned artifacts, metadata, evaluation results, lineage, and approval workflows. - Promotion Path: Define the promotion path from "trained" to "qualified" to "deployed to robot." Evaluation & Qualification Harnesses - Automated Evaluation: Define the offline benchmarks, simulation rollouts, and policy-gating harnesses that any model must pass before reaching Apollo. - Metrics Framework: Develop the metrics framework that the autonomy team trusts to gate releases. Serving, Packaging & Deployment to Robot - On-Robot Path: Own the path from registered model to running inference on Apollo — packaging (ONNX, TensorRT, torch.compile), versioning on-robot, rollback, and observability of deployed policy behavior. - Telemetry Seam: Coordinate with Connect and Data Platform on the deploy-and-telemetry seam back from the fleet. Mentorship & Cross-Functional Leadership - Mentorship: Mentor mid-level and senior engineers on the MLOps team through code review, design review, and direct collaboration. - Influence: Partner with the Training Infrastructure engineer on the cluster/platform contract, and influence research workflows across Autonomy to standardize on the platform's primitives. ## SKILLS AND 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) - Experience leading technical projects end-to-end: architecture, implementation, validation, and iteration - Demonstrated ability to lead by influence across teams — setting standards that other engineers adopt voluntarily, and mentoring engineers around you - Proficiency with cloud infrastructure (AWS, GCP, or Azure), Docker, Git, and modern CI/CD workflows EDUCATION and/or EXPERIENCE - Master's degree in Computer Science, Machine Learning, or a 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, hands-on experience owning an MLOps platform that shipped models to production. Preferred Qualifications: - Experience deploying ML models to edge or embedded targets (on-device inference, ONNX Runtime, TensorRT, robot fleets) - Experience with RL training and evaluation infrastructure for embodied agents (rollout workers, replay buffers, sim-eval harnesses) - Familiarity with humanoid robotics, dexterous manipulation, or teleoperation data domains - Experience with simulation-in-the-loop evaluation (IsaacSim, MuJoCo, or equivalent) - 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 *This is a direct hire.  Please, no outside Agency solicitations. 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. ## About Apptronik ## Company Overview - **One-liner**: Apptronik builds advanced humanoid robots for real-world industrial applications, starting with manufacturing and warehousing, to address labor shortages and improve human productivity. - **Entity Type**: Private (Funding Stage: Series A) - **Headquarters**: Austin, Texas, United States - **Founded**: 2016 - **Founders**: Founded as a spinout from the Human Centered Robotics Lab at the University of Texas at Austin; leadership includes CEO Jeff Cardenas and CTO Nicholas Paine ## Core Business - Primary industry/industries: Robot Manufacturing, Humanoid Robotics, Industrial Automation, Artificial Intelligence - Target customers: B2B / Enterprise — manufacturing facilities, warehousing and logistics operators, supply chain organizations - Mission or purpose statement: “It is not Man vs. Machine, but Man + Machine” — building robots that empower humans to live to their fullest potential by solving labor shortages and reducing physical strain. ## Products & Services - **[Apollo Humanoid Robot Platform]**: A full-electric, bipedal humanoid robot designed for industrial use. Combines advanced hardware, embodied AI, and fleet-level management software. Current applications include material handling, line-side tasks, palletizing, case picking, trailer unloading, and inspection. Built as a scalable platform that can extend into healthcare, eldercare, and home environments over time. (Type: Hardware + SaaS Platform) ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed - **Annual Revenue**: ~$3.5 million (estimated) - **Total Funding**: ~$963.8 million (includes a Seed Round of $14.6M in 2022, a Corporate Round in 2025, and an undisclosed Series A round; notable investors include Mercedes-Benz Group AG and Google) - **Notable Investors/Partners**: Mercedes-Benz Group AG, Google, Terex, Humanoid Global - **Growth Signals**: Headcount grew 98.1% year-over-year to 334 employees (2026 data). Named to the **2026 CNBC Disruptor 50** list. Recognized on the **Best Places to Work** list (2024, 2025). Won the **2025 IEEE Robotics & Automation Product Innovation Award**. Named **Most Innovative** (2025). Active job postings increased 238.5% year-over-year (88 open roles). Acquired Elevate Robotics Inc. ## Competitive Advantages - **Deep heritage**: Spun out of UT Austin’s Human Centered Robotics Lab; team dates back to the DARPA Robotics Challenge (2015) and includes ex-NASA collaborators. - **Full-stack vertical integration**: Covers hardware, embodied AI, and fleet-level management software — a complete, scalable platform rather than a one-off robot. - **Safe, human-centric design**: Built for human environments from the ground up, with a focus on reducing physical strain and complementing human workers, not replacing them. - **Early enterprise traction**: Active partnerships with major industrial players like Mercedes-Benz and Terex, proving real-world deployment over lab demos. ## Strategic Focus - Scaling Apollo for manufacturing and warehouse deployment (current core market). - Expanding into more complex environments: construction, healthcare, eldercare, and the home. - Continuous hardware and software upgrades (CEO Jeff Cardenas calls humanoid robotics “the space race of our time” and has signaled major upgrades to Apollo). - Building out fleet-level tools for multi-robot management and learning. ## Why Work Here - **Mission-driven**: Work on the frontier of humanoid robotics with a stated ethos of “Man + Machine” — robots that improve human quality of life. - **High-growth environment**: Doubled headcount year-over-year with massive funding and expanding job postings; strong career growth potential. - **Culture values**: Passion, creativity, collaboration, integrity, curiosity, and humility are explicitly stated as core values. - **Perks & Benefits**: - Flexible PTO (open, unlimited) - 401k retirement plan - Comprehensive Medical, Dental & Vision coverage - Parental leave - Flexible work hours - Collaborative and innovative work environment - **Remote/Hybrid Policy**: Not explicitly stated; 77% of workforce is in the United States (majority in Austin HQ). Specific roles may vary, but the company promotes a collaborative environment. - **Talent sources**: Attracts talent from top robotics and tech companies — SpaceX, Tesla, Fox Robotics, Agility Robotics, Apple, iRobot, Neocis, and top universities (UT Austin). - **Employee rating**: 3.9/5.0 on employer reviews (Work-Life: 3.5, Career: 3.7). ## Sources 1. [apptronik.com](https://apptronik.com/) — Company homepage 2. [apptronik.com/about-us](https://apptronik.com/about-us) — About Us / History / Mission 3. [apptronik.com/careers](https://apptronik.com/careers?gh_src=B+Capital+job+board) — Careers page / Perks & Culture 4. [apptronik.com/careers/job-listings](https://apptronik.com/careers/job-listings) — Open job listings 5. [linkedin.com/company/apptronik-inc.](https://www.linkedin.com/company/apptronik-inc.) — LinkedIn company profile (revenue, headcount, funding, employee breakdown) ## Other roles at Apptronik - [Principal Robotics Machine Learning Engineer](https://feeny.ai/job/principal-robotics-machine-learning-engineer-apptronik-austin-x92kzcwg7nyv) — Austin, TX - [Staff Simulation Architect - Core Simulation Platform](https://feeny.ai/job/staff-simulation-architect-core-simulation-platform-apptronik-austin-117z0qakax2b) — Austin, TX - [Director, Product Learning & Development](https://feeny.ai/job/director-product-learning-development-apptronik-austin-tk6vhqnh31yt) — Austin, TX - [Global Fleet Operations Specialist](https://feeny.ai/job/global-fleet-operations-specialist-apptronik-austin-zwtj3ybk24q2) — Austin, TX - [Senior Software Engineer, ML Infrastructure](https://feeny.ai/job/senior-software-engineer-ml-infrastructure-apptronik-austin-18dndaxv44yc) — Austin, TX - [Staff Perception Hardware Engineer](https://feeny.ai/job/staff-perception-hardware-engineer-apptronik-austin-19bevj2req6r) — Austin, TX - [ECAD Librarian](https://feeny.ai/job/ecad-librarian-apptronik-austin-gyswfs07ryd6) — Austin, TX - [Staff Video Producer](https://feeny.ai/job/staff-video-producer-apptronik-austin-18bpvt87c36w) — Austin, TX - [Senior Mechanical Engineer (Battery and Docking Station)](https://feeny.ai/job/senior-mechanical-engineer-battery-and-docking-station-apptronik-austin-qfmna3fch3tr) — Austin, TX - [Senior System Integration Engineer (Soft Goods/Gloves/Grip)](https://feeny.ai/job/senior-system-integration-engineer-soft-goods-gloves-grip-apptronik-austin-yfa57ybxj1f7) — Austin, TX