Low Latency Fpga Developer Jobs

NK Securities Research is a leading financial firm that leverages cutting edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High Frequency Trading across different asset classes. With a focus on innovation, entrepreneurship, and collaboration, we aim to foster a dynamic work environment that reflects a startup culture. Role Overview As an FPGA Developer, you will design, implement, and rigorously test RTL (Register-Transfer Level) designs to power our high-frequency trading strategies. Your contributions will play a pivotal role in reducing system latency and enhancing performance, directly impacting trading outcomes. Key Responsibilities Design and Implementation: Develop high-performance RTL designs using VHDL or Verilog for FPGA-based systems. Optimize hardware implementations for ultra-low latency and high throughput. Testing and Debugging: Perform thorough functional and timing testing of RTL designs, ensuring adherence to specifications. Debug and resolve issues using FPGA debugging tools such as SignalTap, ChipScope, or ModelSim. Collaboration and Documentation: Work closely with hardware and software teams to ensure seamless integration of FPGA solutions with the Software Trading Stack. Maintain clear and comprehensive documentation of designs, test cases, results and benchmarks Qualifications Technical Skills: RTL Development: Strong fundamentals in digital logic design, Boolean algebra, FSMs and synchronous design. Proficiency in VHDL or Verilog/SystemVerilog, with a strong focus on efficient and optimized designs. FPGA Tools: Hands-on experience with industry-standard tools such as Xilinx Vivado. Testing and Verification: Expertise in writing testbenches and conducting simulation-based verification. Familiarity with static timing analysis and achieving timing closure. Familiar with CocoTB or any similar testing setup. Debugging: Proficiency in GHDL synthesis a

Role: Network Engineer Location: Gurgaon Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Network Engineer for our team in Gurgaon. Graviton trades across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition to statistical inference analysing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. Key Responsibilities Design, deploy, operate, and troubleshoot low-latency network infrastructure used by trading firms. Manage connectivity to global stock exchanges, brokers, market-data providers, and ISPs. Build and maintain colocation infrastructure including routers, switches, Layer-1 devices (added advantage), structured cabling and cross connects. Configure and support Cisco Nexus, Arista and similar platform devices. Design and troubleshoot Layer 2 and Layer 3 networks including: VLANs, VRFs, BGP, OSPF, Static routing, PIM, IGMP, Multicast, SSM, ACLs and QoS. Troubleshoot packet loss, multicast issues, duplicate packets, IGMP/PIM and multicast/BGP routing. Monitor and optimize latency, jitter, packet loss, interface errors, congestion, and network performance. Work with ultra-low-latency technologies including: Cut-through switching, Layer-1 switches, FPGA-based network devices, Kernel-bypass networking, ExaNIC/Solarflare NICs, Hardware timestamping. Configure and troubleshoot PTP and clock synchronization infrastructure. Perform server and network equipment installation in exchange and third-party data centres. Manage rack layout, patching, cable optimization, optics, DACs, cross-connects, and inventory. Coordinate network changes with exchanges, telecom providers, brokers, vendors, and data-centre teams. Plan and execute production changes during approved maintenance windows. Perform pre-change validation, c

About the Role: As a Staff Software Engineer on the ML Infrastructure team, you will collaborate closely with the Machine Learning and Product teams to build world-class machine learning inference platforms. These platforms power essential services like personalized recommendations, search, and content understanding across Tubi. A core responsibility of this team is developing and maintaining low-latency ML model serving systems that support Deep Learning, LLM, and Search models. This involves building self-service infrastructure and critical components such as the inference engine, feature store, vector store, and experimentation engine. You will improve the way we deploy and operate our services and even contribute to open-source projects. This role grants the architectural freedom to explore new frameworks, lead critical cross-functional projects, and transform the capabilities of our ML and Product teams. Responsibilities: Design and build scalable, high throughput, and low latency distributed systems using Scala Build reusable components and services that serve various ML applications like Personalization, Search, Ads and Exploration Partner closely with ML engineers to understand their challenges and limitations and develop scalable solutions to address them. Proactively recommend solutions to keep our ML Inference stack state of the art. Take a data driven approach to identifying & optimizing latency, cost, and efficiency of our infra. Lead large scale cross functional refactorings if necessary Mentor other engineers on the team on system design, effective incident management, interviewing, leveraging LLMs for work, etc. Collaborate with ML, Product, and cross functional engineering teams to define the long term vision and architecture for ML Infrastructure at Tubi. Your Background: Experience designing and building scalable, distributed systems in any modern backend language (e.g., Scala, Java, Python, Go, C++); experience with Scala or JVM b

Job Overview: We are seeking a high-caliber Rust Systems Engineer to design, build, and optimize high-throughput, low-latency backend systems and infrastructure. In this role, you will go beyond web APIs—tackling low-level performance bottlenecks, complex state management, parallel compute algorithms, and concurrent data processing pipelines. If you thrive on deterministic memory management, zero-cost abstractions, and writing safe, blazingly fast concurrent systems, this role is for you. Key Responsibilities: High-Performance Architecture: Design and implement zero-cost, high-throughput, low-latency engine components and data processing pipelines in Rust. Concurrent & Thread-Safe Systems: Build thread-safe, lock-free, or fine-grained locked data structures and state machines capable of scaling across multi-core architectures without race conditions. Priority & Real-Time Threading: Design custom thread pools, task schedulers, and execution queues with priority-based task scheduling and resource allocation controls. Algorithmic Optimization: Implement complex computational logic, including high-efficiency recursive algorithms, tail-call optimizations, and dynamic cache-friendly data structures. Resource & Memory Management: Leverage Rust’s ownership model, lifetime annotations, custom allocators, and non-blocking I/O to achieve predictable low-latency profiling (minimizing allocations and cash misses). System Profiling & Benchmarking: Conduct continuous benchmarking (criterion), flame graph analysis, memory profiling (Val grind/heap track), and CPU SIMD/vectorization optimizations. Key Skills: Core Rust & Functional Programming: Advanced Rust Mastery: Deep experience with Rust internals (stdsync, stdcell, custom Drop, unsafe Rust boundaries, and macro systems). Closures & Higher-Order Functions: Mastery of Rust’s functional traits (Fn, FnMut, FnOnce), capturing environments, move semantics within closures, and passing unboxed closures for zero

The Site Reliability Engineering team designs and builds the global infrastructure on which we deploy our services, focusing on the above mentioned flagship MongoDB Atlas platform. As our customers grow and globalize, our services must satisfy demands for low-latency requests around the globe, and comply with various data sovereignty requirements. The SRE Team’s mission is to build this increasingly complex infrastructure, while continually lowering the operational burden associated with it, and increasing our internal visibility into the health of the system. We are strong believers in infrastructure-as-code and self-healing systems. The SRE Team is fully integrated with all the other engineering teams, and the teams work closely together with a soft and traversable boundary between their areas of responsibility. We are looking to speak to candidates who are based in New York City for our hybrid working model. Responsibilities Design and build the infrastructure for a global cloud service that comprises hundreds of thousands of MongoDB clusters, processes a billion metrics per day, and replicates tens of billions of database writes to our backup service Design, implement, and troubleshoot the automation and monitoring of services that seamlessly spans the globe - including several cloud providers Become an expert in infrastructure performance, helping us optimize from the application level all the way through the firmware Build for resilience. Our goal is that nobody’s pager goes off, ever. Are we there yet? No. Are we really close? Very. While we work on that - participate in a weekly on-call rotation Improve our infrastructure capabilities, optimizing for cost, simplicity, and maintainability Requirements 3+ years of experience running a mission critical service at scale in a Linux environment Firm grasp of at least one modern programming language, beyond basic scripting Familiarity with web and network protocols and standards (HTTP, TLS, DNS, etc) Bachelor’s deg

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We’re looking for an Infrastructure Security Engineer to design and secure the core systems that power our platform. This role focuses on building security directly into our infrastructure—from container isolation and orchestration to identity and secrets management in a multi-tenant, cloud-native environment. You’ll work closely with engineering teams to define secure primitives and ensure our platform is resilient, scalable, and trustworthy by design. This is a hands-on, deeply technical role focused on real systems, not compliance or policy. What You'll Do: Platform & Runtime Security Design and improve isolation mechanisms for multi-tenant workloads (containers, sandboxing, execution environments) Strengthen boundaries between customers, workloads, and internal systems Identify and mitigate risks in distributed, dynamic compute environments Container &

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll automate the integration of new capacity from a growing set of hardware providers; from auditing and benchmarking hosts and clusters, to maintaining our machine images, configuring GPUs, RDMA, networking, and storage, and getting machines into production. You'll build the automation that keeps the fleet healthy without human intervention: detecting bad GPUs, thermals, and disks. You'll dig into whatever is between the hardware and the software that runs on

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll make cold starts feel local when the data is hundreds of milliseconds away, designing the caching, preloading, and peer-to-peer layers that hide object-store latency and keep public ingress off saturated uplinks. You'll own durability and cost at petabyte scale, from streaming and batch replication between origins, to garbage collecti

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll own the full lifecycle of a machine, from accepting and benchmarking new hardware from a growing set of providers, to network bring-up, kernel and image management, GPU and disk health tracking, and automated remediation of unhealthy hosts. You'll manage a team of 3–8 engineers while staying hands-on across the stack which involves BMCs, firmware, PXE, bootloaders, Linux networking, drivers, and distributed control-plane services, and you'll shape our long-

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll set technical direction for the primitives that other teams (filesystems, training, sandboxes) build on, balancing durability, latency, throughput, and cost. You'll own the roadmap from today's hardest problems (garbage collection at petabyte scale, active-active replication, rate limiting that protects the upstream without wasting ut

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: Modal considers high-quality documentation to be essential for developer experience, and we see docs becoming even more important as agents increasingly deploy and operate Modal Apps. We are looking for a content-minded engineer who will partner with our product teams to curate Modal’s technical documentation and maintain a high quality bar across multiple dimensions. Responsibilities: Thinking holistically about content architecture and how the docs should evolve as Modal introduces new products and features Innovating on novel documentation formats and delivery channels to optimize agent productivity, in collaboration with our Agent DX research team Developing content standards, style guides, and automated enforcement mechanisms to ensure consistent style and high quality Building and maintaining automated pipelines that will enforce the correctness of code examp

NK Securities Research is a leading financial firm that leverages cutting-edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High-Frequency Trading (HFT) across different asset classes. Role Overview We’re looking for engineers who can take AI work beyond experiments and make it hold up in production. You’ll work closely with quant researchers and infra engineers to build AI systems that actually get used improving research speed and internal tooling without slowing down the core stack. We value engineers who think about trade-offs, test what they build, and care about how things run in production. What You’ll Build Production AI Ship models that meet defined latency and reliability expectation Add monitoring, rollback, and guardrails before anything goes live Optimise inference across CPU/GPU environments when it matters Integration into Real Systems Plug AI into data-heavy workflows without hurting performance Work within existing low-latency architecture instead of fighting it Profile and remove bottlenecks rather than guessing AI for Engineers & Researchers Build tools that genuinely speed up research and development Improve code understanding, review workflows, and internal knowledge retrieval Keep systems auditable and predictable LLM & Retrieval Systems Implement structured RAG and embedding pipelines with validation in place Create safe integration layers between models and internal systems Performance & Standards Track latency, drift, and stability — not just accuracy Build observability into everything you ship Help raise the bar for how AI is engineered here What We’re Looking For Strong Python fundamentals Clear thinking around system design and performance trade-offs Experience deploying AI systems in production (1–5 years is typical) Familiarity with transformers, embeddings, or LLM deployment Nice to have: Exposure to C++ / Rust / Go E

Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Senior Quantitative Trader for our team in Singapore. We trade across a multitude of asset classes and trading venues with significant market share and constantly seeking to replicate our successes to newer exchanges and products. As a Senior Quantitative Trader your responsibilities will include Develop and deploy completely automated systematic strategies with short holding periods and high turnover Typical strategies deployed include Alpha-seeking strategies and Market Making Rigorously back-test strategies on in-house research infrastructure Graviton can offer a successful Quantitative Trader Exceptional financial rewards Friendly and collegial working environment Access to advanced trading systems for low-latency execution of strategies Excitement of being a part of a new expanding trading business Requirements : Deep experience in HF/UHF Trading Live HF Trading experience for at least 2 years. PnL Track record with excellent sharpe ratios. Programming experience in C++ or C Proficiency in using Python, R, or Matlab for statistical/data analysis of HFT tick data Possess a degree in a highly analytical field, such as Engineering, Mathematics, Computer Science Benefits: Our open and casual work culture gives you the space to innovate and deliver. Our cubicle free offices , disdain for bureaucracy and insistence to hire the very best creates a melting pot for great ideas and technology innovations. Everyone on the team is approachable, there is nothing better than working with friends! Our perks have you covered. Competitive compensation 4 Weeks of paid vacation Regular after work parties Top of the line health insurance for family International team outing

Description: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Senior Quantitative Trader for our team in Gurgaon. We trade across a multitude of asset classes and trading venues with significant market share and constantly seeking to replicate our successes to newer exchanges and products. As a Senior Quantitative Trader your responsibilities will include Develop and deploy completely automated systematic strategies with short holding periods and high turnover Typical strategies deployed include Alpha-seeking strategies and Market Making Rigorously back-test strategies on in-house research infrastructure Graviton can offer a successful Quantitative Trader Exceptional financial rewards Friendly and collegial working environment Access to advanced trading systems for low-latency execution of strateiges Excitement of being a part of a new expanding trading business Requirements : Deep experience in HF/UHF Trading Live HF Trading experience for at least 2 years. PnL Track record with excellent sharpe ratios. Programming experience in C++ or C Proficiency in using Python, R, or Matlab for statistical/data analysis of HFT tick data Possess a degree in a highly analytical field, such as Engineering, Mathematics, Computer Science Benefits: Our open and casual work culture gives you the space to innovate and deliver. Our cubicle free offices , disdain for bureaucracy and insistence to hire the very best creates a melting pot for great ideas and technology innovations. Everyone on the team is approachable, there is nothing better than working with friends! Our perks have you covered. Competitive compensation 4 Weeks of paid vacation Monthly after work parties Catered breakfast and lunch Fully stocked kitchen Gym membership International team outing

AI/ML – Investment Services A Career with Point72's AI/ML – Investment Services Team The AI/ML – Investment Services team at Point72 spearheads the development of cutting-edge AI solutions that seek to transform our business processes and enhance enterprise intelligence. The team aims to bridge the gap between business challenges and technological innovation, collaborating with stakeholders across the firm and leveraging expertise in generative AI, data engineering, and machine learning. WHAT YOU'LL DO Build and scale core backend services and platforms that power generative AI applications and data infrastructure used across the firm’s investment workflows Design and implement high-throughput, low-latency data pipelines to ingest, normalize, and serve both structured and unstructured data Develop robust APIs and microservices to support model inference, feature serving, and downstream applications Integrate generative AI tools and model-serving workflows into production, including embedding stores, retrieval components, and fine-tuning pipelines Optimize system performance, cost, and reliability through profiling, capacity planning, and architectural improvements Implement automated testing, continuous delivery pipelines, monitoring, and incident response practices to maintain production health Partner with data scientists, AI engineers, product owners, and operations to translate models and prototypes into scalable, production-grade solutions Mentor engineers, lead code reviews, and establish engineering best practices for maintainability, security, and observability Own end-to-end delivery, operational runbooks, and metrics-driven measurement of feature impact and system reliability WHAT'S REQUIRED Bachelor’s degree in computer science, software engineering, or a related technical field Minimum 5+ years of professional experience building backend systems and production services Demonstrated experience designing and operating large-scale data engineering pipelines