Optimize large-model inference at scale: improve throughput, reduce latency and cost per token, build benchmarking harnesses, tune parallelism and quantization strategies, implement load-balancing in routing, and evaluate custom kernels and emerging inference hardware.
About Dragonfly
Dragonfly is a global investment firm focused on the future of money, markets, and intelligence. With origins in crypto and over $3.5 billion in assets under management, we invest in generational companies and protocols across the frontier of finance and technology. We're a team of seasoned VCs, technologists, and markets experts with deep roots in New York, Silicon Valley, and Asia. We've published some of the industry's most widely read research on stablecoins, market structure, decentralized finance, networks, protocol security, and increasingly, the intersection of finance and AI.
This is an application to join our talent network. This is not a listing for an internal role at Dragonfly.
We're actively sourcing for a Senior Inference Optimization Engineer for one of our portfolio companies building privacy-first consumer AI infrastructure. You'll be on the bleeding edge of LLM inference performance, pushing throughput, driving down latency, and optimizing cost per token at significant scale.
Location: Remote, USA (open to excellent candidates outside the USA)
What We’re Looking For:
- 5+ years in performance optimization or HPC with deep GPU architecture and parallel programming knowledge
- Hands-on experience with at least one production LLM inference engine (vLLM, SGLang) running at high volume
- Demonstrated experience with LLM inference optimization: continuous batching, PagedAttention, KV cache management, speculative decoding, quantization, CUDA graphs, torch.compile
- Experience with distributed inference strategies: tensor parallelism, pipeline parallelism, MoE parallelism in multi-GPU and multi-node environments
- GPU profiling fluency: Nsight Systems, Nsight Compute, PyTorch Profiler
- Proficiency in Python, Rust, or Go. C++/CUDA a strong plus
- Bonus: custom Triton kernels, diffusion/image model inference optimization, open-source inference framework contributions
About the role:
- Stand up and optimize GPU infrastructure including B300 nodes in owned data centers
- Drive down TTFT and TPOT, push throughput, and improve cost per token for LLM inference workloads
- Build reproducible benchmarking harnesses across inference engines to identify optimal engine, quantization scheme, and parallelism strategy per workload and GPU SKU
- Optimize multivariate inference load-balancing algorithms within the inference routing system
- Evaluate emerging inference optimization techniques including custom CUDA/Triton kernels, novel attention variants, new quantization schemes, and compilation stack improvements
- Evaluate emerging inference hardware (FPGAs, ASICs, custom silicon) for viability in the stack
Even if you don't match every point above, we encourage you to apply. There may be other opportunities that fit your skill set.
Process:
- We'll review your application and assess fit for this role.
- If there's a match, we'll facilitate a warm introduction to the team.
- If the timing isn't right, we'll keep you in mind for future opportunities across the portfolio.
Compensation
- Early Career: $110,000–$150,000
- Mid: $150,000–$200,000
- Senior: $180,000–$250,000
- Staff/Leadership: $230,000–$330,000
The compensation range reflects variation by seniority, location, and hiring company. Final compensation is confirmed with the specific hiring company.
Submit your information below, and we’ll reach out if there’s a potential fit.
Similar Jobs
Artificial Intelligence • Consumer Web • Digital Media • Information Technology • Social Impact • Software
Build and own evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Lead experiments across prompts, models, routing, caching, cost, and latency. Diagnose failures across planning, tool use, and agent execution, while partnering with AI Core engineering. Manage and grow the AI Quality team as a hands-on player-coach, contributing directly to production code and technical direction.
Top Skills:
Ai AgentsAnnotation WorkflowsBraintrustCachingCi/CdEvaluation FrameworksLangsmithLlmsModel RoutingObservability ToolingPythonRuby On Rails
Enterprise Web • Hardware • Internet of Things • Software
Manage a portfolio of life sciences customer accounts, driving adoption, utilization, operational digitization, measurable ROI, renewals, and commercial expansion. Serve as the internal customer champion and trusted advisor while coordinating Sales, Product, Engineering, Support, Professional Services, and partners. Build executive relationships, lead stakeholder presentations, guide GxP manufacturing change management, and improve Customer Success processes. The role requires life sciences industry experience, enterprise collaboration, and up to 15% travel across the EMEA region.
Top Skills:
AITulip
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical
Leads Pfizer’s medical channel excellence team and develops global omnichannel strategies for scientific engagement across therapeutic areas. Oversees audience journeys, digital and social channels, paid media, vendor relationships, budgets, regulatory alignment, and channel performance. Partners with Medical Affairs, R&D, Evidence Generation, legal, compliance, and global teams to disseminate scientific content to healthcare professionals. Provides strategic leadership, manages resources, develops channel infrastructure, and drives innovation in personalized medical communications.
Top Skills:
Digital PlatformsOmnichannel Engagement PlatformsScientific Content Dissemination ToolsSocial Media ChannelsThird-Party Digital Platforms
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
