Teza Technologies

Location: Austin; Yerevan; London Workplace: OnSite About the role We have an exciting opportunity in the real-time, low-latency trading platform space for an efficient, self-directed, core infrastructure developer with a desire to learn new systems and design techniques. In this role, you will design and implement new functionality as well as optimize and troubleshoot the existing system while working with various team members. We offer competitive compensation and excellent benefits. Key Responsibilities - Design, develop, and support core components of the real-time trading platform: market data, order management, exchange connectivity, and execution infrastructure - Troubleshoot and resolve production issues across a globally distributed trading infrastructure - Develop and maintain Java and C++ trading systems Basic Requirements - 5+ years experience in a front-office trading environment building low-latency systems using Core Java and C++ - Bachelor’s or Master’s degree in Computer Science or related field - Experience with Network Programming (TCP, UDP), binary protocols - Experience with Unix/Linux Development - Experience with multi-threading and lock-free data structures - Experience with some subset of: Market Data, Order Management System, FIX, Algo Trading, Smart Order Router, DMA, and ITCH - Ability to work in a fast-paced, dynamic environment Nice to have - Experience with Python for tooling, analytics, or data pipelines - Experience with Grid Computing / Big Data - Experience with databases (SQL, MongoDB, Redis, or similar) - Experience with cloud infrastructure (AWS) Benefits - Health, visual and dental insurance - Flexible sick time policy

Location: Austin; Yerevan Workplace: OnSite About the role Teza's Data Platform team owns the data the firm trades on: every backtest, every live strategy, every portfolio decision starts with data we ingested, cleaned, stored, and served. The scale, in plain numbers: - 1 PB of raw historical vendor data, growing by ~150 GB every day - 120 TB of processed, query-ready data in historical storage - Thousands of scheduled jobs: run by cron today, actively migrating to Apache Airflow - Alternative data delivered directly into the real-time feeds of live trading strategies This is a hands-on position on a small team of data engineers with growth potential. The firm is looking for outstanding technical skills, strong attention to detail, and a desire to architect and build data platforms. Location Austin, TX / Yerevan, Armenia (in-office requirement) Key Responsibilities - Work directly with Portfolio Managers and Quantitative Developers: turn their requirements into datasets and pipelines, and be the person who knows every nuance of the data they trade on. - Design and onboard new data sources into our warehouse; improve the robustness, speed, and scalability of our systems; manage data entitlements. - Build automated systems for data cleansing, anomaly detection, monitoring, and alerting, bad data must never reach a strategy. - Evaluate new tools and technologies for organizing, querying, and streaming large datasets, and when nothing on the market fits, build it. That's how the bitemporal store happened. - Support the production data warehouse the firm depends on. - Develop and maintain vendor relationships aligned with our business objectives. What we're building right now - A bitemporal data store, designed and written in-house from scratch. Every dataset answers both "what did we know then?" and "what do we know now?", which is what lets researchers trust a backtest. - A Python 3.14 migration of a large, long-lived codebase. - Adoption of the latest Apache Airflow: writing DAGs for the thousands of jobs moving off cron. - Pipelines for market data and alternative data: everything from exchange feeds to weather. - Real-time delivery: alternative data flows straight into strategies' live feeds. Pipelines you build sit in the trading path. - CI/CD for all of it, in GitHub Actions. Our Stack Python and Java · Apache Airflow · Slurm · NATS · PostgreSQL · MongoDB · S3 · NFS · GitHub Actions   Basic Requirements - Proficiency in Python and Unix/Linux for data manipulation, scripting, and automation. - Strong SQL, including query optimization and performance tuning, and familiarity with NoSQL. - A solid grasp of data modeling: normalization and denormalization, and the judgment to know when each applies. Nice to have - Financial industry experience or internships. - Java (part of our platform is written in it). - Experience with on-premises data infrastructure. - Familiarity with a cloud platform (AWS or GCP). - Apache Airflow or similar workflow orchestration tools. Benefits - Health, visual and dental insurance - Flexible sick time policy

Location: Austin; Yerevan; London Workplace: OnSite We are looking for exceptional quantitative researchers to develop systematic trading strategies based on market microstructure. This role is focused on extracting predictive signals from high-frequency market data and turning them into robust, scalable trading models. You will explore large datasets, develop new features, test hypotheses, and work closely with researchers and engineers to deploy ideas into production. Location Austin, TX, Yerevan, AM and London, UK (5 days in-office requirement) Key Responsibilities - Research predictive signals from market microstructure data. - Design and evaluate new features using the most granular market data. - Develop statistical and machine learning models for systematic trading. - Build robust research infrastructure and analytical tools. - Work directly with experienced researchers and Portfolio Managers to take ideas from hypothesis to live trading. Basic Requirements - PhD in Mathematics, Statistics, Physics, Computer Science, Electrical Engineering, or a related quantitative discipline. - Outstanding mathematical and statistical skills. - Strong programming ability in Python and C++/Java. - Experience working with quantitative models. - Curiosity, creativity, and proven academic track record. What you’ll get - On-site presence of experienced Quantitative Researchers and Portfolio Managers to learn from - Build Strategies while becoming the best at what you do - Professional guidance from experienced mentors Benefits - Health insurance - Flexible sick time policy - Office Lunches

Location: Austin; London Workplace: OnSite We're looking for an experienced Portfolio Manager to build and scale systematic trading strategies across global agricultural markets. This role is ideal for someone who combines deep knowledge of agricultural commodity fundamentals with a quantitative mindset. You'll have the opportunity to develop differentiated alpha by integrating supply and demand dynamics, weather, satellite imagery, logistics, positioning, and alternative datasets into systematic trading models. You'll work alongside experienced quantitative researchers, engineers, and data specialists while having the autonomy to shape research direction, portfolio construction, and execution. Location Austin, TX or London, UK (onsite) Key Responsibilities - Develop and manage systematic trading strategies across agricultural commodity markets. - Generate alpha by combining fundamental commodity research with quantitative modeling and statistical analysis. - Build predictive models using datasets such as weather, crop conditions, satellite imagery, freight flows, inventories, positioning, macroeconomic data, and other alternative data sources. - Design and evaluate new signals through rigorous research, backtesting, and performance attribution. - Continuously improve portfolio construction, risk allocation, and execution efficiency. - Partner closely with quantitative developers to productionize research and build scalable trading systems. - Work across the full research lifecycle, from idea generation through live deployment and ongoing monitoring. Basic Requirements - Deep understanding of agricultural commodity markets, including supply and demand dynamics, seasonal effects, logistics, weather, and global trade flows. - Several years of experience managing or researching systematic commodity strategies. - Strong quantitative background with experience applying statistical or machine learning techniques to financial markets. - Excellent Python programming skills and the ability to work with large datasets. - Experience building systematic investment strategies rather than discretionary trading alone. - Strong understanding of portfolio construction, risk management, and systematic execution. - Intellectual curiosity and a passion for developing new sources of alpha. Nice to Have - Experience trading multiple commodity sectors beyond agriculture. - Familiarity with alternative datasets, remote sensing, satellite imagery, or geospatial analytics. - Experience with machine learning applied to commodity forecasting. - Knowledge of real-time research infrastructure and production trading systems. - Graduate degree in a quantitative discipline. - Track record of managing external or proprietary capital. Benefits - Health, visual and dental insurance - Flexible sick time policy

Location: Yerevan Workplace: OnSite About the role Teza is a systematic trading firm building quantitative strategies across multiple asset classes. We are looking for a DevOps Engineer to own and evolve our infrastructure platform — the systems that our quants, developers, and traders rely on every day. Our infrastructure team is at an inflection point. We have a working platform that supports active trading and research, and we are now investing in making it more robust, observable, and developer-friendly. The successful candidate will have significant influence over the direction of our infrastructure — shaping tooling choices, establishing standards, and building systems that scale with the firm. Location Yerevan, Armenia, Austin, Texas or London, UK What We're Building Toward As a team, we are working toward the following goals. You will play a central role in defining the approach and driving execution: - Full-stack observability** across all internal services — unified metrics, centralized logging, distributed tracing, and actionable alerting — so that engineers and traders have clear, real-time visibility into system health and performance. - Reliable, self-service compute orchestration** spanning Slurm (HPC/ML workloads), Hadoop (batch data processing), and Airflow (workflow scheduling) — enabling researchers and data engineers to run workloads at scale without infrastructure bottlenecks. - Mature secrets management** for trading credentials, API keys, certificates, and service-to-service authentication — with rotation policies, auditing, and tight integration into deployment workflows. - Unified release pipelines** that bring consistency to how diverse applications — trading strategies, data pipelines, and real-time trading systems — move from development to production, each with their own build, test, and deployment needs. - A well-maintained platform foundation** where shared services — identity management, GitHub Actions runners, VPN, observability tooling — stay current and reliable without disrupting active trading. - Strong security posture** across production and research environments — network segmentation, access controls, vulnerability management, and compliance — that evolves alongside the platform rather than being bolted on after the fact. Requirements - Experience building and operating internal platform services for development teams (CI/CD, compute, monitoring, developer tooling) — not just consuming them. - Strong proficiency in Linux systems administration and container orchestration (Docker, Kubernetes, or similar). - Deep understanding of the software development lifecycle and how infrastructure supports engineering teams — from local development through CI to production deployment. - Familiarity with ML and data-intensive workflow requirements: GPU scheduling, large dataset access patterns, experiment tracking, and reproducible compute environments. - Proficiency with at least one major cloud provider (AWS, GCP, or Azure) including networking, IAM, and managed services. - Experience designing and operating hybrid infrastructure — cloud, on-premises, and colocation environments — with an understanding of the tradeoffs between them. - Hands-on programming ability in Python or another scripting language, sufficient to build tooling, automation, and infrastructure-as-code — not just run playbooks. - Solid understanding of core network protocols and services: DNS, LDAP, SMTP, TLS, HTTP, and SSH. - Practical knowledge of infrastructure security: firewall management, access control models (zero-trust, bastion hosts), vulnerability scanning, patch management, and audit logging. Nice to Have - Experience in a trading firm or other environment with strict uptime and latency requirements. - Familiarity with infrastructure-as-code tools (Terraform, Pulumi, Ansible). - Experience with log aggregation and SIEM systems. - Understanding of compliance frameworks relevant to financial services. Benefits - Health, visual and dental insurance - Flexible sick time policy

Location: Austin; Yerevan; London Workplace: OnSite About the role Teza is a systematic trading firm developing quantitative strategies across multiple asset classes. We are looking for a Lead Infrastructure Engineer to own the platform that supports our traders, quants, and developers. This includes colocation hardware near the exchanges, research compute and developer services, and the security and release processes that connect these environments. This is a player-coach role. You will lead the infrastructure team, which includes DevOps Engineers and Infrastructure Engineers, and set its technical direction while remaining hands-on. You will provision and tune bare-metal trading servers, respond to production incidents, and review the designs and solutions your team delivers. We are not looking for a manager who delegates all technical work or an engineer who avoids leading people. We need someone who can do both and determine where their time and attention are most needed. Our platform supports active trading and research today, and we are investing in making it more robust, observable, secure, and developer-friendly. You will have significant ownership and decision-making over infrastructure tooling, standards, and priorities across the firm. Location: Austin, Texas (On-Site) What You'll Own: - Trading Infrastructure and Colocation. Oversee server procurement and lifecycle management across multiple exchange colocations. Select low-latency NICs, such as Solarflare, Exanic, and Mellanox, and kernel-bypass technologies. Develop and maintain Linux tuning standards for trading hosts, including CPU isolation, NUMA-aware pinning, IRQ affinity, and BIOS and kernel settings. Manage hardware failures, spare capacity, and capacity planning. - Research and Developer Platform. Oversee compute orchestration for researchers and data engineers, including Slurm for HPC, machine learning, and GPU workloads; Hadoop for batch processing; and Airflow for workflow scheduling. Support shared services such as JupyterHub, databases, artifact storage, and large-dataset storage. Determine when Kubernetes is appropriate and when alternative approaches are better suited. - Security. Establish and maintain access controls for production trading hosts in colocation. Manage network segmentation across colocation, corporate, and research environments. Oversee secrets management for exchange credentials, API keys, and certificates, including rotation, auditing, and deployment integration. Lead vulnerability management, patching for systems that cannot be routinely rebooted, intrusion detection, and incident response. - Releases and Deployment. Define how trading strategies, data pipelines, and real-time trading systems move from development into production. Establish rollback procedures and release processes for infrastructure changes, including kernels, firmware, and NIC drivers. Oversee infrastructure-as-code pipelines and coordinate releases with the trading calendar. - Operational Maturity. Establish the on-call model and escalation paths. Improve observability across internal services through metrics, logging, tracing, and alerting. Lead incident response and blameless post-mortems, ensuring action items are tracked and completed. - Hybrid Infrastructure. Oversee colocation infrastructure for trading, along with on-premises and cloud environments supporting research and corporate workloads. Evaluate the tradeoffs between these environments, manage cloud costs, and establish disaster recovery plans with defined recovery time objectives (RTOs) and recovery point objectives (RPOs). - The Team. Hire, develop, and retain infrastructure engineers. Set priorities across trading performance and latency, research compute needs, and operational tooling. Represent the infrastructure team and its priorities across the firm. What We're Building Toward: These are the shared goals of the infrastructure team. DevOps Engineers and Infrastructure Engineers execute against them, while you set the technical approach, establish priorities, and drive progress. - Full-stack observability across internal services, including unified metrics, centralized logging, distributed tracing, and actionable alerting. - Reliable, self-service compute orchestration across Slurm, Hadoop, and Airflow, enabling researchers and data engineers to run workloads at scale without unnecessary infrastructure bottlenecks. - Mature secrets management for trading credentials, API keys, certificates, and service-to-service authentication, with rotation, auditing, and deployment integration. - Unified release pipelines that standardize how trading strategies, data pipelines, and real-time trading systems move from development to production. - A well-maintained platform foundation that includes identity management, GitHub Actions runners, VPN, and observability tooling, with updates delivered safely and without disrupting active trading. - A strong security posture across production and research environments, with security practices that evolve alongside the platform rather than being added after the fact. Basic Requirements: - Experience operating infrastructure for a trading firm or another environment with strict uptime and latency requirements, including hands-on experience with colocation hardware and bare-metal Linux servers. - Deep, demonstrable expertise in low-latency Linux, including kernel and network tuning, CPU and NUMA isolation, IRQ affinity, and kernel-bypass NICs and technologies such as Solarflare/Onload, Exanic, and Mellanox/DPDK. You must also have the measurement discipline to validate performance improvements. - Experience leading an infrastructure, DevOps, or SRE team, including hiring, setting priorities, and taking accountability for outcomes while remaining a hands-on technical contributor. - Experience building and operating internal platform services for development and research teams, including CI/CD, compute, storage, monitoring, and developer tooling. Experience consuming these services alone is not sufficient. - Strong Linux systems administration skills and experience with container orchestration technologies such as Docker and Kubernetes. You should understand where containers are appropriate in a trading environment and where they are not. - Familiarity with machine learning and data-intensive workflows, including GPU scheduling, large-dataset access patterns, and reproducible compute environments. - Experience designing and operating hybrid infrastructure across colocation, on-premises, and at least one major cloud provider, such as AWS, GCP, or Azure. You should understand the tradeoffs between these environments. - A practical, mature approach to infrastructure security, including access control models such as zero-trust and bastion hosts, network segmentation, secrets management, vulnerability scanning, patch management, audit logging, and incident response. - Hands-on programming experience in Python or another scripting language, with the ability to build tooling, automation, and infrastructure-as-code solutions rather than simply execute existing playbooks. - A solid understanding of core network protocols and services, including TCP/IP, multicast market data delivery, DNS, LDAP, Kerberos, TLS, HTTP, and SSH. - A track record of managing structured on-call and incident response processes, including escalation paths, post-mortems, and follow-through on action items.   Nice to Have: - Experience with FPGA-based or other hardware-accelerated market data and order-entry systems. - Experience with HPC storage and networking, including NFS at scale, parallel filesystems, and InfiniBand. - Familiarity with infrastructure-as-code tools such as Terraform, Pulumi, and Ansible. - Experience with log aggregation and security information and event management (SIEM) systems. - Understanding of compliance frameworks relevant to financial services.   Benefits -  Health, visual and dental insurance -  Flexible sick time policy

Location: Austin Workplace: OnSite We are looking for Quantitative Researchers to join our quant hub in Austin. You will have a chance to build alphas, and conduct research working with datasets covering different markets. The role demands sharp analytical skills, a relentless commitment to excellence, and a passion for uncovering hidden patterns in the data. Our team values determination, precision, and the ability to think critically and creatively. While the work is demanding, the rewards are significant, both in the impact of your contributions and the growth you’ll achieve in this collaborative and high-performance environment. We rely on people’s autonomy and provide freedom to create the best algorithms in finance, while truly being attentive to a fundamentally important asset - communication. Location Austin, TX (5 days in-office requirement) Key Responsibilities - Build statistical and predictive models - Manage all aspects of the research process, including methodology selection, data collection/normalization and analysis, prototyping, backtesting, and performance monitoring - Apply rigorous modeling techniques to explore variety of datasets - Build new and improve existing signals (long-term) - Optimize quantitative code and numerical methods - Adapt, fix, write tests, and in some cases re-implement strategies execution code Basic Requirements - Physics, Mathematics, Computer Science, Engineering or other technical degree - Math skills: statistics, linear algebra, optimization etc - Strong critical thinking skills with an analytical and creative approach to problem-solving - Practical Knowledge of Python and Python libraries (NumPy, Pandas etc) - Detail-oriented approach - Written and Verbal English in full-working-proficiency level Nice to have Requirements - Phd / MSc degree in Physics, Mathematics, Computer Science, Engineering or similar area - Practical knowledge of C++ or Java - Competitive Programming Experience (participation in International STEM Olympiads is a huge plus) What you’ll get - On-site presence of experienced Quantitative Researchers and Portfolio Managers to learn from - Build Strategies while becoming the best at what you do - Professional guidance from experienced mentors What makes you a match - You LOVE solving technical and math problems! - You love to code and play with data - You are willing to explore new articles and implement suitable ideas - Difficult problems make you excited - You are not afraid of unknown and love to learn - You have A LOT of passion and drive Benefits - Health insurance - Flexible sick time policy - Office Lunches