Staff Software Engineer

Vaga não está no LinkedIn

🕒 Ontem

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $150.000 - $250.000 / ano

⏰ Tempo Integral

🔴 Especialista

🧑‍💻 Engenheiro Full-stack

🦅 Patrocina Visto H1B

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Helm.ai

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2016

🤖 Inteligência Artificial

🚗 Transporte

🤝 B2B

🔥 Investimento no último ano

💰 Corporate Round - Helm.ai em 2025-10

Artificial Intelligence • Transport • B2B

A Helm. ai é uma empresa de software de autonomia com foco em IA que desenvolve pilhas de percepção e autonomia prontas para produção baseadas exclusivamente em visão e modelos de simulação generativa para ADAS até direção autônoma de Nível 4. A empresa fornece software em tempo real veicular (Helm. ai Vision, Helm. ai Driver) e ferramentas de treinamento/validação offline (GenSim-2, VidGen-2, WorldGen-1) treinadas com seu aprendizado não supervisionado Deep Teaching™ e IA generativa para escalar treinamento, simulação, resolução de casos extremos e implantação para projetos voltados para produção. A Helm. ai faz parcerias com montadoras globais, como Honda e Volkswagen, para oferecer soluções autônomas escaláveis, com uma abordagem centrada na visão.

Descrição

• Lead and Build: Architect and design scalable, reliable, and efficient ML infrastructure and services, enabling the company to handle large scale training, massive datasets, and a growing number of customers with diverse needs. • Scale Systems: Ensure the ML platform is capable of handling large-scale data processing and machine learning model development at a global scale. • Agility & Cost Efficiency: Optimize platform implementation for flexibility and cost-effectiveness while maintaining agility in development, enabling quick adaptation to evolving requirements. • User-Centric Design: Build systems that are convenient, reliable, and easy to use for both internal teams (data scientists, engineers) and external customers. • End-to-End Ownership: Take full ownership of the platform's lifecycle, from inception through design and implementation to deployment and monitoring. • Collaboration: Work closely with cross-functional teams, including data science, product, and operations, to ensure seamless integration of ML capabilities into business-critical services. • Innovation: Keep up with the latest industry trends and incorporate cutting-edge technologies into our platform to maintain a competitive edge. • Problem-Solving: Tackle complex technical challenges head-on, from infrastructure optimization to providing solutions for data processing, storage, and access at scale. • Mentorship: Lead and mentor engineers, fostering a culture of excellence and high-performance within the engineering team.

🎯 Requisitos

• Proven experience in building and scaling cloud-based infrastructure and services, particularly in machine learning or data-heavy environments. • Expert knowledge of distributed systems, cloud platforms (AWS, GCP, Azure), and technologies like Kubernetes, Docker, and microservices architecture. • Deep experience with building large scale services, and the challenges that come with performance, reliability, and cost at scale. • Demonstrated ability to design and implement cost-effective solutions while balancing performance, security, and scalability. • Solid background in machine learning concepts, e.g., model training and validation. • Strong leadership and collaboration skills, with experience working in an agile development environment and mentoring high-performing teams. • Ability to manage ambiguity and thrive in a fast-paced, evolving environment where priorities shift rapidly. • Passionate problem solver with a focus on building practical, reliable, and efficient systems that can scale in real-world, production environments.

🏖️ Benefícios

• Competitive health insurance options • 401K plan management • Remote-friendly and flexible team culture • Free lunch and fully-stocked kitchen in our South Bay office • Additional perks: monthly wellness stipend, office set up allowance, company retreats, and more to come as we scale • The opportunity to work on one of the most interesting, impactful problems of the decade