helm-ai - Staff Software Engineer
Requirements
• 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.
Responsibilities
• 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.
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
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