Jobs at Bot Auto

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.

Supports corporate IT operations through user onboarding and offboarding, asset inventory, laptop deployment, help desk support, monitoring and alert triage, patching, software deployment, documentation, and office IT logistics. The intern assists with hardware, software, access, conference room AV, desk setups, and equipment moves while gaining exposure to enterprise tooling, networking, identity management, and infrastructure automation.

Build and improve AI agents that automate engineering, fleet operations, and business workflows. Responsibilities include integrating LLMs with internal systems, implementing planning and tool-using agent workflows, developing backend services and APIs, evaluating models and frameworks, and contributing to internal AI platforms. The role requires strong programming fundamentals, Python or comparable language skills, and familiarity with machine learning or LLMs. Preferred experience includes agent frameworks, RAG, vector databases, cloud platforms, and containerization.

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 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.

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.

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.

Manage full-cycle technical recruiting for a Bay Area startup: source, screen, coordinate interviews, maintain ATS, partner with hiring managers, build pipelines for software, ML, robotics and autonomous driving roles, support events and university outreach, and improve recruiting processes.