Location: In-Person (sftwtrs.ai Lab) Experience Level: Early Career / 1–3 years About sftwtrs.ai sftwtrs.ai is a leading AI lab focused on security automation, adversarial machine learning, and scalable AI-driven solutions for enterprise clients. Under the guidance of our Principal Scientist, we combine cutting-edge research with production-grade development to deliver next-generation AI products in cybersecurity and related domains. Role Overview As a Research Engineer I , you will work closely with our Principal Scientist and Senior Research Engineers to ideate, prototype, and implement AI/ML models and pipelines. This role bridges research and software development: you’ll both explore novel algorithms (especially in adversarial ML and security automation) and translate successful prototypes into robust, maintainable code. This position is ideal for someone who is passionate about pushing the boundaries of AI research while also possessing strong software engineering skills. Key Responsibilities Research & Prototyping Dive into state-of-the-art AI/ML literature (particularly adversarial methods, anomaly detection, and automation in security contexts). Rapidly prototype novel model architectures, training schemes, and evaluation pipelines. Design experiments, run benchmarks, and analyze results to validate research hypotheses. Software Development & Integration Collaborate with DevOps and MLOps teams to containerize research prototypes (e.g., Docker, Kubernetes). Develop and maintain production-quality codebases in Python (TensorFlow, PyTorch, scikit-learn, etc.). Implement data pipelines for training and inference: data ingestion, preprocessing, feature extraction, and serving. Collaboration & Documentation Work closely with Principal Scientist and cross-functional stakeholders (DevOps, Security Analysts, QA) to align on research objectives and engineering requirements. Author clear, concise documentation: experiment summaries, model design notes, code review comments, and API specifications. Participate in regular code reviews, design discussions, and sprint planning sessions. Model Deployment & Monitoring Assist in deploying models to staging or production environments; integrate with internal tooling (e.g., MLflow, Kubeflow, or custom MLOps stack). Implement automated model-monitoring scripts to track performance drift, data quality, and security compliance metrics. Troubleshoot deployment issues, optimize inference pipelines for latency and throughput. Continuous Learning & Contribution Stay current with AI/ML trends—present findings to the team and propose opportunities for new research directions. Contribute to open-source libraries or internal frameworks as needed (e.g., adding new modules to our adversarial-ML toolkit). Mentor interns or junior engineers on machine learning best practices and coding standards. Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or a closely related field. Research Experience: 1–3 years of hands-on experience in AI/ML research or equivalent internships. Familiarity with adversarial machine learning concepts (evasion attacks, poisoning attacks, adversarial training). Exposure to security-related ML tasks (e.g., anomaly detection in logs, malware classification using neural networks) is a strong plus. Development Skills: Proficient in Python, with solid experience using at least one major deep-learning framework (TensorFlow 2.x, PyTorch). Demonstrated ability to write clean, modular, and well-documented code (PEP 8 compliant). Experience building data pipelines (using pandas, Apache Beam, or equivalent) and integrating with RESTful APIs. Software Engineering Practices: Familiarity with version control (Git), CI/CD pipelines, and containerization (Docker). Comfortable writing unit tests (pytest or unittest) and conducting code reviews. Understanding of cloud services (AWS, GCP, or Azure) for training and serving models. Analytical & Collaborative Skills: Strong problem-solving mindset, attention to detail, and ability to work under tight deadlines. Excellent written and verbal communication skills; able to present technical concepts clearly to both research and engineering audiences. Demonstrated ability to collaborate effectively in a small, agile team. Preferred Skills (Not Mandatory) Experience with MLOps tools (MLflow, Kubeflow, or TensorFlow Extended). Hands-on knowledge of graph databases (e.g., JanusGraph, Neo4j) or NLP techniques (transformer models, embeddings). Familiarity with security compliance standards (HIPAA, GDPR) and secure software development practices. Exposure to Rust or Go for high-performance inference code. Contributions to open-source AI or security automation projects. Why Join Us? Cutting-Edge Research & Production Impact: Work on adversarial ML and security–automation projects that go from concept to real-world deployment. Hands-On Mentorship: Collaborate directly with our Principal Scientist and Senior Engineers, learning best practices in both research methodology and production engineering. Innovative Environment: Join a lean, highly specialized team where your contributions are immediately visible and valued. Professional Growth: Access to conferences, lab resources, and continuous learning opportunities in AI, cybersecurity, and software development. Competitive Compensation & Benefits: Attractive salary, health insurance, and opportunities for performance-based bonuses. How to Apply Please send a résumé/CV, a brief cover letter outlining relevant AI/ML projects, and any GitHub or portfolio links to careers@sftwtrs.ai with the subject line “RE: Research Engineer I Application.” sftwtrs.ai is an equal-opportunity employer. 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