Helm.ai is building the next generation of AI technology for ADAS, autonomous driving, and robotics automation.
Machine Learning Engineer
Location
United States
Posted
98 days ago
Salary
$150K - $250K / year
Seniority
Senior
Job Description
Machine Learning Engineer
Helm.ai
• Collaborate with researchers to perform research operations using existing infrastructure • Use judgment in complex scenarios and help apply standard techniques to a wide variety of technical problems • Characterize neural network quality, failure modes, and edge cases based on research data • Maintain awareness of current trends in relevant areas of research and technology • Coordinate with researchers and accurately convey status of experiments • Manage a large number of concurrent experiments and make accurate time estimates with respect to deadlines • Review experimental results and suggest theoretical or process improvements for future iterations • Write technical reports indicating qualitative and quantitative results to external parties
Job Requirements
- A sense of practical optimism: not all experiments are successful, but the ones that are more than make up for it!
- Proficiency in Python
- Proven ability to thrive in fast-paced environment
- Ability to communicate complex technical concepts to colleagues and a variety of audience
- Introspection, thoughtfulness, and detail-orientation
- Master’s or Ph.D. in a related field and/or 5+ years of experience in a directly related field (a plus)
- Experience reading research papers and implementing the techniques therein (a plus)
- Computer vision and deep learning experience (a plus)
Benefits
- Competitive health insurance options
- 401K plan management
- 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
Related Guides
Related Job Pages
More Machine Learning Engineer Jobs
• Design, debug, and maintain ML systems in realistic, tools-enabled environments • Work across training, evaluation, and infrastructure to ensure ML systems behave correctly and robustly in practice
• Design and build evaluation systems that assess educational AI quality across thousands of conversations - from learning outcomes to bias detection to curriculum alignment • Build ML systems that enable self-improving app creation - learning from high-quality apps on the platform to automatically scaffold new applications for educators • Design and prototype downloadable, on-device AI models that work without internet connectivity - critical for privacy and global accessibility • Develop systems that enable dynamic, fluid interfaces adapting to learning moments - transitioning seamlessly from chat to writing editor to interactive physics simulation as needed • Build content moderation and safety systems designed specifically for educational discourse • Implement agentic AI systems that enable educators to create goal-directed applications (e.g., "help students through this project over 2 weeks") • Build sophisticated RAG systems that integrate diverse educational content with semantic search and knowledge graphs
• Token Factory is a part of Nebius Cloud, one of the world's largest GPU clouds, running tens of thousands of GPUs. • We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. • Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs. • Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. • Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines. • Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.
• Ship and support AI-native products from concept to production. • Apply sound judgment on building and maintaining high-performing production ML systems. • Stay deeply informed on the latest machine learning research and developments. • Collaborate with a team of strong engineers; serve as a mentor and resource for other high-potential ML engineers.
