Research Engineer

Who We Are

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.

Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.

We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.

Our Values

Move Fast: We act with speed and precision, breaking down big challenges into achievable steps.
Focus: We complete one goal at a time with care, collaborating as a team to deliver features with precision.
Balance: Sustained performance comes from rest and recovery. We ensure a healthy work-life balance to keep you at your best.
Craftsmanship: Innovation through excellence. Every detail matters, and we take pride in mastering our craft.
Minimal: Simplicity drives our innovation. We eliminate complexity through discipline and focus on what truly matters.

What We're Looking For

We are seeking a highly skilled Research Engineer to help optimize 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 work 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 problem solving with hands-on implementation. Successful candidates are comfortable working broadly across the stack — from model behavior and inference systems to distributed infrastructure and developer tooling — while collaborating closely with customers and internal engineering teams to solve complex AI performance challenges.

This role is based in one of our hubs (NYC, SF, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites.

What You'll Do

Optimize large-scale training and inference workloads across GPUs, accelerators, and distributed systems
Work directly with customers to analyze workloads, identify bottlenecks, and improve performance, scalability, and reliability of deployed AI systems
Develop and improve inference pipelines, model serving systems, and performance-oriented tooling for production AI workloads
Design and implement profiling, debugging, and observability tools to analyze model execution and guide optimization strategies
Work across the software stack to ensure performance improvements are accessible through clean APIs, automation, and seamless integration with the Lightning ecosystem
Partner with hardware vendors and ecosystem partners to support efficient execution across diverse compute backends (NVIDIA, TPU, and emerging accelerators)
Contribute 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

What You’ll Need

Required Qualifications

Strong expertise with deep learning frameworks such as PyTorch
Experience working with large-scale training or inference workloads
Familiarity with 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 analyzing and improving performance bottlenecks in ML systems, infrastructure, or distributed workloads
Excellent collaboration and communication skills, including the ability to work cross-functionally and partner directly with customers or external contributors
Ability to work comfortably in ambiguous, fast-moving environments and operate across multiple layers of the stack
Bachelor’s degree in Computer Science, Engineering, or a related field

Nice-to-Haves

Experience with inference optimization techniques such as quantization, speculative decoding, mixed precision, memory-efficient training, or throughput/latency optimization
Experience with technologies such as CUDA, Triton, TensorRT, vLLM, SGLang, Dynamo, or related ML systems/inference tooling
Experience contributing to open-source ML, infrastructure, or AI systems projects
Startup experience or experience working in highly cross-functional environments
Advanced degree (Master’s or PhD) in AI, machine learning, systems, or related fields

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:

$120,000 - $250,000 USD

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:

$120,000 - $250,000 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.
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