Jobs at Bot Auto

Build and operate security across autonomous trucking systems, including vehicle security, cloud infrastructure, Kubernetes, CI/CD, IAM, secrets management, vulnerability management, AI security, threat modeling, monitoring, and incident response. The role contributes to secure software development, system hardening, OTA updates, supply-chain protection, and security automation across onboard and platform environments.

Designs and operates security across autonomous trucking systems, including onboard vehicle security, cloud and infrastructure security, IAM, secrets management, secure OTA updates, AI safeguards, vulnerability management, threat modeling, detection, and incident response. The role also establishes secure development practices, automates security tooling, and mentors engineering teams.

Develops production-grade embedded software for vehicle control units in autonomous trucks. Responsibilities include requirements and architecture, model-based development with MATLAB/Simulink, embedded C/C++, AUTOSAR integration, vehicle controls, diagnostics, fault management, and MIL/SIL/HIL validation. The role also involves CI/CD automation, bench and vehicle debugging, CAN-based communication, cross-functional collaboration, and technical mentorship.

Serves as the Houston office’s first point of contact and manages daily workplace operations. Responsibilities include greeting visitors, handling mail and shipping, maintaining supplies and common areas, coordinating building vendors, managing conference rooms and badges, supporting new-hire setup, ordering meals, organizing events, booking travel, tracking purchases, and assisting People and BizSup projects. The role is on-site Monday through Friday and requires strong organization, communication, discretion, and follow-through.

Develop and operate internal Kubernetes-based platforms, infrastructure automation, workflow systems, observability tooling, API gateways, and messaging platforms. Support data, simulation, ML, CI/CD, and LLM workflows; improve reliability, cost efficiency, and platform visibility. Participate in on-call rotations, incident response, documentation, and internal user enablement while receiving mentorship and contributing production code.

Develop and maintain onboard infrastructure for autonomous driving systems, including sensor drivers, health management, middleware, diagnostics, logging, recovery, and real-time data handling. Optimize autonomous driving software for embedded systems and onboard computing platforms, focusing on low latency, deterministic performance, computational efficiency, reliability, and system integration.

Develop and improve autonomous truck planning and control algorithms using C++ and Python. Investigate driving issues through simulation, vehicle data, logs, and code analysis; design rigorous experiments; analyze failures and regressions; document reproducible results; and collaborate with validation and simulation engineers on testing and autonomy-stack integration.

Manage enterprise IT infrastructure across offices, data centers, endpoints, and SaaS environments. Lead IAM, system reliability, patching, MDM, virtualization, storage, ITSM workflows, and automation initiatives. Lead and mentor a small IT team while remaining hands-on with escalations, projects, technical direction, workload planning, and on-call operations. Support security compliance, audit readiness, and autonomous trucking operations.

Owns core platforms that deliver autonomous driving software from development through vehicle deployment. Responsibilities include C++ build and packaging systems, CI/CD, Docker and Kubernetes infrastructure, cloud and edge services, APIs, event-driven architecture, identity and access control, PKI, mTLS, secrets management, and fleet operations. The role also involves code review, architectural design, infrastructure on-call, mentoring, and cross-team technical leadership.

Architect, build, deploy, and operate production-grade AI agentic systems and workflows. Responsibilities include backend and full-stack development, agent orchestration, workflow state, retrieval, memory, approvals, failure recovery, data architecture, enterprise integrations, cloud infrastructure, observability, security, and scaling. The role establishes reusable engineering patterns and partners with users to deliver reliable AI products, while addressing production issues involving model behavior, permissions, latency, cost, and consistency.

Lead functional and system safety engineering for a Level 4 autonomous trucking platform. Responsibilities include hazard analysis, safety requirements, safety cases, risk assessments, FMEA, fault-tree and STPA analyses, ODD evaluation, verification and validation strategy, supplier coordination, launch readiness, incident analysis, and safety governance. The role partners across autonomy, hardware, software, vehicle integration, operations, fleet, and testing teams to ensure safety risks are identified, mitigated, traceable, and supported by evidence for deployment decisions.

Perform state-of-the-art prototyping and benchmarking of multi-modal perception, mapping, and prediction models. Own a focused research project from data analysis through model verification. Train, tune, and optimize deep learning models on large-scale compute and truck datasets under senior mentorship.

Develop, train, and optimize deep learning models for autonomous driving (perception, mapping, end-to-end planning). Execute full ML lifecycle from data curation to deployment, collaborate with simulation and infrastructure teams, and evaluate SOTA research to address real-world corner cases.

Develop and research deep learning and vision algorithms for autonomous driving (detection, tracking, prediction, planning, SLAM). Lead end-to-end ML projects from data analysis through model evaluation, and collaborate across product, simulation, and algorithm teams to extend ML technologies into system components.

Build high-performance, scalable simulation infrastructure and tooling for autonomous vehicle development. Design systems for scenario generation, simulation execution, synthetic data, and reinforcement-learning workflows. Collaborate with ML, perception, and controls teams to improve realism, performance, and validation pipelines while writing production-quality C++ and Python code, contributing to architecture, testing, and documentation.

Develop and train conditioned policies and MARL systems to simulate realistic driving behaviors, implement safety-constrained RL algorithms, design rewards and evaluation metrics, optimize large-scale training pipelines, advance neural architectures for long-horizon planning and spatial reasoning, and integrate research models with production simulation and planning teams.

The Senior ML/RL Engineer will develop behavior models, implement RL algorithms focusing on safety, design reward functions and optimize training environments while collaborating across teams.

Optimize end-to-end GPU performance for real-time autonomous driving workloads: develop and optimize parallel GPU algorithms and inference pipelines, profile and eliminate bottlenecks across computation and memory, debug GPU software for latency/throughput improvements, and collaborate on onboard GPU software architectures for embedded platforms.