5 Latency Optimization Jobs

You are a skilled C++ developer with over 4 years of experience, specializing in high-performance, low-latency systems. Your expertise lies in modern C++ (C++14/17/20), multithreading, and concurrency. You have a strong background in Qt development, particularly in building real-time, high-performance trading user interfaces. Your experience includes creating ultra-fast order execution engines, market data feeds, and real-time risk management tools. In addition to your technical skills, you possess a deep understanding of networking protocols such as TCP/IP, UDP, and FIX, as well as interprocess communication methods like IPC, shared memory, and message queues. You have hands-on experience in latency optimization, performance tuning, and utilizing tools like perf, Valgrind, and gprof. Proficiency in memory management, lock-free programming, and CPU cache optimization is also part of your skill set. You have a hacker mentality and enjoy tackling challenging problems. Your responsibilities will include architecting, developing, and optimizing ultra-low-latency C++ trading applications capable of handling millions of transactions per second. You will build high-performance market data processing solutions with microsecond-level latencies and develop real-time, intuitive, and high-speed trading interfaces using Qt. Your work will involve exchange connectivity, FIX protocol integrations, and risk management systems. You will be expected to profile and optimize code to achieve maximum throughput and minimal latency, working alongside an elite team to solve some of the hardest engineering problems in the fintech industry. Experimenting with new technologies to stay ahead of the competition and owning your work end-to-end, from concept to deployment, are also key aspects of this role. Ideally, you have experience in high-frequency trading (HFT), market-making, or ultra-low-latency environments. Knowledge of exchange matching algorithms, order routing strategies, and market microstructure will be beneficial. Contributions to open-source C++ and Qt projects or performance-critical software, as well as expertise in hardware acceleration (FPGA, SIMD, AVX, GPU computing), are highly valued. Familiarity with cloud-based trading infrastructure and hybrid on-prem/cloud systems is a plus. As part of a high-energy startup with significant growth potential, you will work with visionary fintech leaders and top-tier engineers to build industry-defining products that will shape the future of trading. The culture values bold ideas, rapid execution, and relentless optimization. If you are passionate about performance, enjoy pushing speed barriers, and aspire to contribute to something significant, this is an opportunity to be part of a team that is reshaping the future of trading. Join us in disrupting the industry together. Apply now for this full-time position with a day shift schedule and an in-person work location.,

We empower our people to stay resilient and relevant in a constantly changing world. We are looking for individuals who are always seeking creative ways to grow and learn, individuals who aspire to make a real impact, both now and in the future. If this resonates with you, then you would be a valuable addition to our dynamic international team. As a Graduate Trainee Engineer, you will have the opportunity to contribute significantly by: - Designing, developing, and optimizing NLP-driven AI solutions using cutting-edge models and techniques such as NER, embeddings, and summarization. - Building and operationalizing RAG pipelines and agentic workflows to facilitate intelligent, context-aware applications. - Fine-tuning, prompt-engineering, and deploying LLMs (such as OpenAI, Anthropic, Falcon, LLaMA, etc.) for specific domain use cases. - Collaborating with data scientists, backend developers, and cloud architects to construct scalable AI-first systems. - Evaluating and integrating third-party models/APIs and open-source libraries for generative use cases. - Continuously monitoring and enhancing model performance, latency, and accuracy in production environments. - Implementing observability, performance monitoring, and explainability features in deployed models. - Ensuring that solutions meet enterprise-level criteria for reliability, traceability, and maintainability. To excel in this role, you should possess: - A Master's or Bachelor's degree in Computer Science, Machine Learning, AI, or a related field. - Exposure to AI/ML, with expertise in NLP and Generative AI. - A solid understanding of LLM architectures, fine-tuning methods (such as LoRA, PEFT), embeddings, and vector search. - Previous experience in designing and deploying RAG pipelines and collaborating with multi-step agent architectures. - Proficiency in Python and frameworks like Lang Chain, Transformers (Hugging Face), Llama Index, Smol Agents, etc. - Familiarity with ML observability and explainability tools (e.g., Tru Era, Arize, Why Labs). - Knowledge of cloud-based ML services like AWS Sagemaker, AWS Bedrock, Azure OpenAI Service, Azure ML Studio, and Azure AI Foundry. - Hands-on experience in integrating LLM-based agents in production settings. - An understanding of real-time NLP challenges (streaming, latency optimization, multi-turn dialogues). - Familiarity with Lang Graph, function calling, and tools for orchestration in agent-based systems. - Exposure to infrastructure-as-code (Terraform/CDK) and DevOps for AI pipelines. - Domain knowledge in Electrification, Energy, or Industrial AI would be advantageous. Join us in Bangalore and be part of a team that is shaping the future of entire cities, countries, and beyond. At Siemens, we are a diverse community of over 312,000 minds working together to build a better tomorrow. We value equality and encourage applications from individuals who reflect the diversity of the communities we serve. Our employment decisions are based on qualifications, merit, and business requirements. Bring your curiosity and creativity to Siemens and be a part of shaping tomorrow with us. Explore more about Siemens careers at www.siemens.com/careers and discover the digital world of Siemens at www.siemens.com/careers/digitalminds.,

As an experienced Quantitative Researcher/Developer, you will be responsible for enhancing and scaling live IBKR TWS algorithms. Your role will involve end-to-end strategy development, including math-driven model design, backtesting, low-latency implementation, and continuous performance tuning. Your key responsibilities will include auditing, troubleshooting, and optimizing existing TWS/IBKR Python strategies for equities, options, and futures. You will also design and deploy high-frequency trading/low-latency strategies using statistical, machine learning, and signal-processing techniques. Additionally, you will employ time-series analysis, probability modeling, VaR, tail-risk, and skewness/drawdown metrics for quantitative analysis. In terms of infrastructure and automation, you will build robust data pipelines, integrate with IBKR's API, and automate end-to-end backtest/live-trade processes. Collaboration with the trading desk to translate research into production-grade code and mentor junior quants will also be a key aspect of your role. The ideal candidate should have at least 5 years of professional experience as a quantitative researcher/developer in electronic equities, options, or futures markets. Deep expertise in IBKR TWS API & Python, high-frequency trading strategies, statistics, mathematics, and experience with US market live-tested algos are required. Proficiency in engineering best practices such as version control, CI/CD pipelines, code review, and performance profiling is essential. Preferred skills include proficiency in Python/C++ for latency-sensitive components, knowledge of cloud infrastructure for scaling back tests, and familiarity with machine learning frameworks for signal generation. For the interview process, you may be asked questions related to algorithm debugging, latency optimization, statistical modeling, risk management, and live strategy examples to assess your skills and experience in quantitative research and development. Overall, this role requires a strong background in quantitative research and development, expertise in algorithm optimization, statistical modeling, risk management, and collaboration with trading desks to drive the enhancement and scaling of live IBKR TWS algorithms.,

As a Machine Learning Engineer at Skyfall, you will play a crucial role in deploying and optimizing large language models (LLMs) in production environments. Your responsibilities will include deploying post-trained LLMs, optimizing inference for cost and latency, and building scalable training pipelines using cutting-edge technologies like DeepSpeed, Accelerate, and Ray. The Skyfall team, comprising of industry pioneers from Maluuba, is committed to revolutionizing the AI ecosystem by creating the first world model for the enterprise. By overcoming the limitations of existing LLMs, the Enterprise World Model aims to provide enterprises with a comprehensive understanding of the intricate relationships between data, people, and processes within organizations. You will be part of a dynamic team spread across New York, Toronto, and Bangalore, working on designing distributed training infrastructure, managing multi-cloud ML deployments, and implementing advanced model compression techniques. Additionally, you will develop internal tools to facilitate multi-GPU training and large-scale experimentation, ensuring efficient resource allocation and continuous model evaluation. To excel in this role, you should have a minimum of 3 years of experience in ML engineering, model deployment, and large-scale training. Proficiency in vector databases such as FAISS, Pinecone, and Weaviate for retrieval-augmented generation (RAG) is essential. Experience with multi-cloud ML deployment, hands-on deployment of large-scale models in production, and expertise in multi-GPU training and inference optimizations are key requirements. Your strong knowledge of ML system performance tuning, latency optimization, and cost reduction strategies will be instrumental in developing cluster management tools for external compute infrastructure and implementing state-of-the-art model compression techniques. By staying abreast of LLM fine-tuning techniques, RLHF, and model evaluation metrics, you will contribute to Skyfall's mission of disrupting the AI landscape and providing enterprises with significant value through innovative solutions.,

BestEx Research is a financial technology and research firm that specializes in developing sophisticated execution algorithms and transaction cost modeling tools across various asset classes. The company offers high-performance algorithmic execution services to hedge funds, CTAs, asset managers, and banks through both a traditional electronic broker and a broker-neutral Software as a Service (SaaS) model. The firm's cloud-based platform, Algo Management System (AMS), is a comprehensive end-to-end algorithmic trading solution designed for equities and futures. It encompasses various features such as transaction cost analysis (TCA), a tool for algorithm customization called Strategy Studio, a trading dashboard, and pre-trade analytics all within a single platform. Currently, the platform is operational for U.S., Europe, and Canadian equities as well as global futures trading. BestEx Research aims to revolutionize the $100 billion industry by challenging conventional black-box solutions from banks and offering innovative execution algorithms that prioritize performance enhancement, transparency, and customization. Leveraging cutting-edge technology, the company supports its low-latency and highly scalable research and trading systems with backend operations in C++, research libraries in C++/Python and R, and web-based technologies for front-end platforms. The mission of BestEx Research is to establish itself as the leader in automation and measurement of execution across global asset classes while significantly reducing transaction costs for clients. The company's Bangalore office serves as a core engineering and research hub, tackling complex challenges in trading, systems, and data science alongside the U.S. team. Joining BestEx Research offers a unique opportunity to work in a collaborative environment with minimal bureaucracy, allowing engineers to directly engage with traders, researchers, and the CEO. Employees benefit from direct ownership and visibility on production systems, daily learning opportunities from industry pioneers, and a high-trust culture where performance takes precedence over hierarchy. The company offers competitive compensation in India, including equity and cash bonuses, along with a structured 5-week training program covering various aspects of market microstructure, algorithmic execution, exchange simulators, and more. As a member of the engineering team at BestEx Research, you will be involved in building ultra-low-latency trading systems, real-time exchange simulators, execution algorithms, and alpha forecasting models. The role requires ownership of the technology stack, ranging from C++ infrastructure to Python-based research platforms. Ideal candidates are those who excel at the intersection of research and engineering, comfortable working on systems, testing hypotheses, and analyzing market-moving data. Key responsibilities include architecting and implementing execution algorithms across global markets, developing exchange simulators and backtesting frameworks, designing smart order routing systems, creating models for market impact and price prediction, optimizing high-performance trading systems for throughput and latency, and establishing core infrastructure to support new asset classes and global markets. Collaboration with global quants, traders, and senior engineers is essential to analyze system performance across various layers. Qualifications for this role include a Bachelors or Masters degree from a top-tier CS, Math, or Engineering program (IIT/NIT/BITS preferred but not required), at least 5 years of hands-on C++ experience in performance-sensitive applications, a strong foundation in data structures, OS, networks, and concurrency, and a passion for learning about markets, systems, and modeling. Additional experience with Python, real-time systems, and trading platforms is advantageous. Working at BestEx Research offers exposure to real-time trading systems, mentorship from industry veterans, a blend of research, systems design, and algorithm development, equity and cash compensation, and a culture free of red tape and outsourcing mentality.,