We are seeking a highly skilled Lead Research Engineer to lead optimization efforts for training and inference workloads running on Lightning AI infrastructure. This role sits at the intersection of ML systems, AI infrastructure, performance engineering, and practical research. You’ll drive improvements across models, inference systems, and platform infrastructure to improve performance, scalability, and reliability for real-world AI workloads. This is a highly cross-functional role that combines deep technical leadership with hands-on implementation. Successful candidates are comfortable operating broadly across the stack — from model behavior and inference systems to distributed infrastructure and developer tooling — while partnering closely with customers and internal engineering teams to solve complex AI systems challenges at scale.
This role is based in one of our hubs (NYC, SF, London, or Seattle — NYC and London are preferred), with a minimum of 2 in-office days per week and occasional team and company offsites.
What You'll Do
Lead optimization efforts for large-scale training and inference workloads across GPUs, accelerators, and distributed systems
Partner directly with customers to analyze workloads, identify bottlenecks, and drive improvements in performance, scalability, and reliability of deployed AI systems
Architect and improve inference pipelines, model serving systems, and performance-oriented tooling for production AI workloads
Lead the design and implementation of profiling, debugging, and observability tools to analyze model execution and guide optimization strategies
Drive performance improvements across the software stack through clean APIs, automation, and seamless integration with the Lightning ecosystem
Collaborate cross-functionally with infrastructure, product, and research teams to shape technical direction and improve the developer and user experience for AI workloads running on Lightning
Partner with hardware vendors and ecosystem partners to support efficient execution across diverse compute backends (NVIDIA, TPU, and emerging accelerators)
Contribute technical leadership to open-source projects through new features, tooling improvements, documentation, and community engagement
Stay current with advancements in large-scale inference, distributed training, and ML systems optimization, and help guide adoption of new technologies and approaches
What You’ll Need
Required Qualifications
Strong expertise with deep learning frameworks such as PyTorch
Significant experience working with large-scale training or inference workloads
Strong understanding of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling, distributed inference)
Strong software engineering fundamentals, including designing APIs, building tooling, debugging complex systems, and shipping production-quality code
Experience leading or driving performance optimization efforts across ML systems, infrastructure, or distributed workloads
Hands-on experience with inference optimization techniques such as quantization, mixed precision, speculative decoding, memory-efficient training, or throughput/latency optimization
Experience with modern ML systems and inference tooling such as TensorRT, vLLM, SGLang, Dynamo, Triton, DeepSpeed, or related technologies
Excellent collaboration and communication skills, including the ability to partner directly with customers, cross-functional teams, and external contributors
Ability to operate effectively in ambiguous, fast-moving environments and drive technical direction across multiple layers of the stack
Master’s or PhD in Computer Science, AI, Machine Learning, Systems, Engineering, or a related field
Nice-to-Haves
Experience contributing to or leading open-source ML, infrastructure, or AI systems projects
Experience working closely with hardware vendors or accelerator ecosystems
Startup experience or experience operating in highly cross-functional environments
Experience mentoring engineers or leading technical initiatives across teams
Compensation
We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits. The anticipated annual base salary range for this role is:
$225,000—$275,000 USD
Benefits and Perks
We offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success. Benefits may vary by location, team, and role. Benefits include:
Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.)
Retirement and financial wellness support (U.S.); Pension contribution (U.K.)
Generous paid time off, plus holidays
Paid parental leave
Professional development support
Wellness and work-from-home stipends
Flexible work environment
At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
LocationLondon, England, United Kingdom; New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States
