Staff Software Engineer
- Role
- Backend
- Experience
- Staff
- Employment
- Full-time
Open to US only. Set where you work from to check your eligibility.
No BS summary
Staff-level software engineer for a remote US role. Must have deep experience building scalable ML infrastructure, distributed systems, cloud platforms (AWS/GCP/Azure), Kubernetes and Docker. Will lead engineering efforts and mentor juniors.
Core skills
Required skills
We help make autonomous technologies more efficient, safer, and accessible.
Helm. Our Deep Teaching™ methodology is uniquely data and capital efficient, allowing us to surpass traditional approaches. Our unsupervised learning software can train neural networks without the need for human annotation or simulation and is hardware-agnostic. We work with some of the world's largest automotive manufacturers and we've raised over $100M from Honda, Goodyear Ventures, Mando, and others to help us scale.
Our team is made up of people with a diverse set of experiences in software and academia. We work together towards one common goal: to integrate the software you'll help us build into hundreds of millions of vehicles.
We're looking for a Staff Software Engineer to join our growing team! At Helm. You will work on cutting-edge technologies, solve complex engineering challenges, and contribute directly to shaping our products and the overall direction of the company. We need people who are organized, able to think on their feet, and can find creative solutions to challenging problems.
You will:
- 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.
You have:
- 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.
The pay range for this position is estimated to fall in the base range of approximately $150,000 and $250,000. Base compensation for this position will vary based on location, qualifications, and relevant experience. The offered base salary may be above or below this range and compensation for the position may include additional compensation in the form of equity or a bonus/commission.
We offer:
- 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
Helm. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Any unsolicited resumes/candidate profiles submitted through our website or to personal email accounts of employees of Helm.
What you'll do
- 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.
What they require
- 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.
Benefits
- 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
Helm.ai builds AI software for autonomous driving and robotics. Its Deep Teaching methodology is data and capital efficient, using unsupervised learning software to train neural networks without human annotation or simulation, and it works with large automotive manufacturers.