Job Openings

Join us in pushing the boundaries of algorithmic trading. Below are our current open positions:

1. Senior Quantitative Researcher

Location: On-site (New York, NY) or Remote
Department: Quantitative Research

About the Role

As a Senior Quantitative Researcher at HAUB Technologies, you will architect and refine cutting-edge trading models. You will collaborate with research and engineering teams to turn market insights into profitable strategies. This role demands extensive knowledge of quantitative methods, coding, and market microstructure.

Responsibilities

• Model Architecture & Development: Design and implement sophisticated quantitative models (time-series, cross-sectional, etc.) in Python or R, incorporating advanced statistical and machine learning techniques.

• Backtesting & Strategy Validation: Use frameworks like zipline or backtrader and distributed computing platforms (Spark, Ray) to validate strategy performance at scale.

• Data Analysis & Feature Engineering: Employ pandas, NumPy, SQL, and other libraries (e.g., statsmodels) to uncover alpha-generating insights.

• Collaboration: Work closely with the engineering team to ensure seamless integration of new research into live trading systems.

• Performance Monitoring: Develop post-trade analytics dashboards to track key performance metrics and risk exposures in real time.

Qualifications

• Education: Master’s or PhD in Mathematics, Statistics, Computer Science, Physics, or related quantitative disciplines.

• Technical Expertise: Proficiency in Python (NumPy, pandas, scikit-learn, PyTorch) and familiarity with R, MATLAB, or C++.

• Finance Knowledge: Deep understanding of market microstructure, portfolio construction, and risk management.

• Analytical Skills: Strong background in probability, stochastic processes, time-series analysis, and optimization.

2. Algorithmic Trading Developer

Location: On-site (New York, NY) or Remote
Department: Software Engineering / Trading

About the Role

We are seeking an Algorithmic Trading Developer adept in building high-performance, low-latency trading systems. You will work closely with quants and traders to ensure our automated strategies run efficiently across various asset classes.

Responsibilities

• Systems Programming: Develop and optimize trading engines in C++17/20 or Rust, focusing on concurrency, scalability, and minimal latency.

• Exchange Connectivity & Protocols: Implement and maintain FIX or proprietary exchange APIs, manage market data feeds (ITCH, OUCH).

• Real-Time Monitoring & Logging: Integrate frameworks such as log4cxx, spdlog, and observability tools (Grafana, Prometheus) to monitor system performance.

• Code Quality & Testing: Conduct peer reviews, use modern build systems (CMake, Bazel), and maintain rigorous unit/integration tests.

• Collaboration: Integrate new signals and models from the quantitative research team into production trading environments.

Qualifications

• Education: Bachelor’s or Master’s in Computer Science, Software Engineering, or related field.

• Technical Stack Expertise: Proficiency in C++, Linux system programming, and network/socket programming. Familiarity with Python is a plus.

• Performance Optimization: Knowledge of CPU caching, lock-free data structures, and kernel-level tuning.

• Low Latency Mindset: Prior experience in high-frequency trading or real-time system development is preferred.

3. Data Scientist (Algorithmic Trading)

Location: On-site (New York, NY) or Remote
Department: Data Science

About the Role

HAUB Technologies needs a Data Scientist to drive large-scale data collection, cleaning, and analysis. You will provide insights that inform both trading strategies and risk management.

Responsibilities

• Data Engineering & Pipeline Development: Build ETL pipelines using Airflow, Luigi, or Prefect for large datasets; utilize Kafka, RabbitMQ, or other streaming platforms for real-time ingestion.

• Feature Engineering & ML Prototyping: Experiment with machine learning algorithms (regression, classification, clustering) using Python (pandas, scikit-learn) or R.

• Statistical Analysis & Visualization: Employ statsmodels or R for advanced statistical modeling; create dashboards with Tableau, Power BI, or Plotly.

• Collaboration with Quants: Transform raw data into actionable features and support model validation efforts.

• Data Governance & Security: Uphold data integrity, versioning, and best practices for sensitive financial data.

Qualifications

• Education: Bachelor’s or Master’s in Data Science, Statistics, Computer Science, or related field.

• Technical Stack: Python (pandas, NumPy, scikit-learn) or R; distributed data storage (Hadoop, Spark, AWS S3).

• Machine Learning Expertise: Familiarity with TensorFlow, PyTorch, or XGBoost is a plus.

• SQL & NoSQL: Proficiency in complex queries (PostgreSQL, MySQL) and basic familiarity with NoSQL databases (MongoDB, Cassandra).

4. Machine Learning Engineer (Trading Applications)

Location: On-site (New York, NY) or Remote
Department: Engineering / Research

About the Role

As a Machine Learning Engineer, you will operationalize advanced ML models within demanding, real-time trading environments. Your efforts focus on robust model deployment, performance optimization, and continuous integration of new models and data streams.

Responsibilities

• Model Deployment: Containerize ML models using Docker, orchestrate with Kubernetes, and deploy on-prem or in the cloud (AWS, GCP, Azure).

• MLOps & Automated Workflows: Implement CI/CD pipelines (e.g., Jenkins, GitHub Actions) for model validation, testing, and streamlined deployment.

• Performance Optimization: Use GPU acceleration (NVIDIA CUDA, RAPIDS) or model compression (quantization, ONNX) to minimize inference latency.

• Monitoring & Retraining: Build dashboards (Prometheus, Grafana) for real-time performance metrics, drift detection, and automatic retraining triggers.

• Collaborative Development: Work closely with Data Scientists and Quants to translate proof-of-concept models into stable production services.

Qualifications

• Education: Bachelor’s or Master’s in Computer Science, Engineering, or a related field.

• Technical Stack: Proficiency in Python ML frameworks (TensorFlow, Keras, PyTorch), containerization (Docker), and orchestration (Kubernetes).

• Software Engineering Best Practices: Experience with Git, code reviews, testing frameworks, and design patterns for large-scale ML systems.

• Performance Conscious: Prior experience in HPC or real-time applications is an advantage.

5. Risk Manager (Algorithmic Trading)

Location: On-site (New York, NY) or Remote
Department: Risk Management

About the Role

As the Risk Manager, you will be responsible for maintaining real-time risk controls, conducting scenario analyses, and ensuring compliance with regulatory standards, all while optimizing our risk-adjusted returns.

Responsibilities

• Risk Analytics Platform: Implement automated risk calculations (VaR, PnL, greeks) in Python or R, integrating with central data repositories or live dashboards.

• Real-Time Monitoring: Set up dashboards (Tableau, Grafana) to track positions, exposures, and threshold breaches, triggering automated alerts as needed.

• Scenario Analysis & Stress Testing: Use MATLAB, R, or Python to model extreme market events and guide hedging strategies.

• Risk Control Implementation: Define margin requirements, position limits, stop-loss rules, and coordinate with trading systems for fail-safe triggers.

• Reporting & Compliance: Generate detailed reports for internal stakeholders and ensure adherence to SEC, CFTC, and other relevant regulations.

Qualifications

• Education: Bachelor’s or Master’s in Finance, Economics, Mathematics, or related field.

• Risk & Trading Expertise: 4+ years of experience in risk management, preferably in a hedge fund or high-frequency trading environment.

• Technical Proficiency: Programming in Python, R, or MATLAB; comfort with SQL/NoSQL databases.

• Analytical Tools: Familiarity with advanced financial mathematics, volatility modeling, and stress testing frameworks.

6. DevOps Engineer (Trading Infrastructure)

Location: On-site (New York, NY) or Remote
Department: Infrastructure / IT

About the Role

We are searching for a DevOps Engineer to design, automate, and maintain the critical infrastructure supporting HAUB Technologies’ high-performance trading systems. You will ensure minimal downtime, robust security, and efficient scalability.

Responsibilities

• Infrastructure Architecture: Manage Linux servers (CentOS, Ubuntu) tuned for low-latency workloads; maintain high-throughput networks (10G/40G/100G).

• CI/CD Pipelines & Configuration Management: Implement continuous integration (Jenkins, GitHub Actions) and infrastructure-as-code (Terraform, Ansible, Chef).

• Monitoring & Observability: Deploy Prometheus, Grafana, and the ELK stack (Elasticsearch, Logstash, Kibana) for comprehensive metrics and logging.

• Security & Compliance: Collaborate with InfoSec to secure configurations, schedule regular patching, and ensure compliance with financial regulations.

• Performance Tuning & Troubleshooting: Optimize kernel parameters, BIOS settings, and quickly resolve production incidents alongside trading teams.

Qualifications

• Education: Bachelor’s or Master’s in Computer Science, Information Systems, or related technical discipline.

• Technical Stack: Linux administration, Docker/Kubernetes, cloud platforms (AWS, GCP, Azure), and scripting in Python or Bash.

• Automation & IaC: Advanced knowledge of Terraform, Ansible, or Chef for automated deployments.

• Low-Latency Focus: Familiarity with kernel tuning, HPC, or hybrid on-prem/cloud setups is highly valued.

How to Apply

For any of the above roles, please send your resume/CV and a brief cover letter to careers@haubtech.com with the subject line: “[Position Title] Application.” Include links to your portfolio, GitHub, or research papers if applicable.

If you have any questions or need more information, feel free to reach out at info@haubtech.com. We appreciate your interest in HAUB Technologies and look forward to hearing from you!