Who You Are:

You're a hands-on data engineer with several years of experience building and operating production data pipelines. You're fluent in Python or Java and comfortable across the modern data stack , AWS, Databricks/Spark, dbt, and Airflow. You care about data quality, write clean and well-tested code, and like owning systems end-to-end. You're curious about crypto and energized by working at the intersection of finance and emerging technology.

What You’ll Do:

• Partner across Galaxy’s business lines (Trading, Custody, Asset Management, Mining) to build and enhance a reliable and scalable data platform
• Design, build, and maintain scalable data pipelines (batch and streaming)
• Develop and operate high-throughput, low-latency data processing systems
• Work within the Databricks and Spark ecosystem to support large-scale data workloads
• Build and maintain cloud-native data solutions on AWS
• Leverage dbt for transformation, data modeling, testing, and documentation
• Use Airflow (or similar orchestration tools) to manage and schedule data workflows
• Write clean, efficient, and well-tested code, following best practices in software engineering
• Collaborate closely with product, analytics, and infrastructure teams to deliver impactful data solutions
• Contribute to technical design discussions and help improve system performance, scalability, and reliability
• Support production systems by troubleshooting issues and implementing improvements
• Embrace and champion the thoughtful adoption of AI to improve team performance and business outcomes.
• Leverage AI tools (e.g., generative AI, automation platforms, data copilots) to improve productivity, decision-making, and output quality in your day-to-day work.

What We’re Looking For:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 5+ years of professional experience in data engineering or distributed systems
• Strong hands-on coding experience in Python, Java, or similar languages
• Experience building and operating both batch and streaming data pipelines
• Strong experience with AWS, Databricks/Spark, and modern data architectures
• Hands-on experience with dbt for transformations and Airflow (or similar) for orchestration
• Solid understanding of data modeling, data quality, and production data systems
• Experience working with relational and non-relational databases (e.g., Postgres, SQL Server)
• Familiarity with CI/CD, testing frameworks, and monitoring tools

Bonus Points: 

• Experience with dependency injections frameworks, ideally: Spring, Guice, HK2
• Experience with Docker
• Full lifecycle development of a commercial system, including: requirements definition, agile planning, releasing, and production support
• Experience in Crypto

What We Offer:

• Competitive base salary and discretionary bonus
• Flexible Time Off (paid)
• Hybrid Working Arrangements 
• Company-paid health and protective benefits for employees and their eligible dependents
• Free coaching and counseling sessions through Headspace
• Generous paid Parental Leave
• Free daily snacks in-office and weekly lunches 
• Opportunities to learn about the Crypto industry
• Smart, entrepreneurial, and fun colleagues
• Employee Resource Groups

*Benefits may vary depending on location.

We are seeking a talented and innovative Synthetic Data Engineer. In this role, you will design and implement domain-specific synthetic data generation pipelines, ensuring high-quality data management for training loops. Your expertise will drive the success of data processing and model training within the organization.

Responsibilities:

• Design domain-specific synthetic data generation (SDG) pipelines via self-instruct and constitutional prompting.
• Implement automated quality scoring and de-duplication systems.
• Manage data pipelines that feed directly into SFT and DPO training loops.

Qualifications:

• Proven experience building large-scale data pipelines (Airflow, Spark, Ray).
• Deep knowledge of prompt engineering for data generation.
• Familiarity with dataset distillation and bias mitigation.

About the role 

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. 

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). 

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. 

Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

• Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). 
• Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
• Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 

2. MLOps& Data Infrastructure: 

• Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. 
• Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
• Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

3. Cross-Functional Implementation & Leadership:

• Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. 
• Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
• Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 

4. Technical Strategy & Stakeholder Management:

• Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. 
• Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
• Vector Database: Experience with Vector DBs for RAG applications. 

Why Join Us? 

• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. 
• Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. 
• Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. 
• Compensation: Competitive salary and benefits package, commensurate with experience and performance. 

Application Process 

• If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
• If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn. 

We are representing a leading force in the decentralized exchange (DEX). We are seeking a seasoned Data Engineer to spearhead the development of robust data architectures. In this role, you will be responsible for the end-to-end lifecycle of data, from ingestion to insight, ensuring our infrastructure is both high-performing and scalable to meet the demands of a fast-paced business environment.

Key Responsibilities

• Design & Architecture: Architect, build, and maintain scalable data pipelines and sophisticated ETL processes to support complex data ecosystems.

• Infrastructure Optimization: Enhance data warehouse performance and implement rigorous data quality frameworks to ensure "single source of truth" reliability.

• Strategic Partnership: Collaborate closely with Product, Engineering, and Go-to-Market teams to translate business requirements into actionable data solutions.

• Operational Excellence: Develop automated monitoring systems to proactively identify and resolve pipeline bottlenecks.

• Agile Response: Execute high-priority, ad-hoc data requests and critical analyses with speed and precision to support urgent stakeholder decision-making.

Required Qualifications & Skills

• Professional Experience: Minimum of 7+ years in dedicated Data Engineering roles, ideally within a high-growth environment.

• Advanced Data Modeling: Deep expertise in data architecture, warehouse design (e.g., Star/Snowflake schemas), and modern ETL/ELT best practices.

• Technical Stack: Mastery of the modern data stack, including Snowflake, Airflow, and dbt.

• Cloud & Infrastructure: Strong proficiency with AWS services and experience managing distributed systems.

• Stream Processing: Hands-on experience with data streaming technologies such as Kafka or Kinesis to optimize large-scale workflows.

• Problem Solving: A proven ability to navigate ambiguity and deliver high-quality solutions under tight deadlines.