Sr. Machine Learning Engineer

Accountabilities: Develop and implement machine learning and data engineering solutions that accelerate applied data science initiatives. Support LLM post-training, custom model development, rigorous evaluation workflows, and production implementation. Design and maintain scalable data pipelines supporting advanced machine learning and data science use cases. Build high-quality solutions for customer-facing AI applications operating at significant scale and low latency. Analyze systems and data to identify potential vulnerabilities, performance gaps, reliability issues, and opportunities for improvement. Develop and maintain distributed systems capable of supporting large-scale AI workloads and inference. Own engineering work end-to-end, including development, testing, deployment, monitoring, and ongoing optimization. Apply strong software engineering practices, including automated testing, peer code review, logging, observability, and resilient architecture. Collaborate with data scientists, engineers, product teams, and other stakeholders to define problems and develop practical technical solutions. Explore and implement improvements to product architecture, knowledge models, user experience, performance, and reliability. Contribute to technical discussions, architecture decisions, and continuous improvement across the engineering organization. Mentor fellow engineers while actively sharing knowledge and learning from teammates. Stay current with emerging machine learning technologies and identify opportunities to apply them effectively. Maintain a strong understanding of customer challenges and translate those needs into scalable engineering improvements. Requirements Professional experience in data engineering and architecture supporting advanced data science or machine learning applications. Deep understanding of LLM post-training techniques and the computational architectures required to support them. Strong understanding of scalability and distributed systems concepts, including sharding, partitioning, concurrency, and large-scale inference. Experience with a high-level programming language such as Python or JVM-based technologies. Experience working with cloud, containerization, and modern infrastructure technologies; relevant technologies include Docker, Kubernetes, AWS, GCP, or managed AI services. Familiarity with technologies such as Kafka, Cassandra, Spark, Elasticsearch, Terraform, Chef, or Ansible is valuable. Experience scaling machine learning inference across GPUs or GPU clusters is highly relevant. Strong software engineering fundamentals, including testing strategies, code reviews, continuous integration, logging, monitoring, and resilient system design. Ability to work effectively in a test-driven, collaborative, and iterative development environment. Demonstrated ability to deliver high-quality, maintainable software consistently and meet project commitments. Strong communication and teamwork skills, with the ability to collaborate effectively across engineering and data science disciplines. Demonstrated use of AI technologies to improve decision-making, streamline workflows and processes, increase efficiency, or drive business outcomes. Strong learning mindset and willingness to develop expertise in new technologies and cybersecurity concepts. Experience with scalable architectures for LLM post-training or fine-tuning is a plus. Prior cybersecurity or intelligence experience is advantageous but not required. Benefits Base salary range of $140,000–$215,000 per year for U.S. candidates. Eligibility for bonuses and equity grants. Comprehensive health insurance and benefits package. 401(k) program. Paid time off and competitive vacation and leave programs. Paid parental and adoption leave. Physical and mental wellness programs. Professional development opportunities available across career levels and roles. Employee networks, geographic community groups, and volunteering opportunities. Remote work arrangement within the United States. Opportunity to work on advanced AI and machine learning systems at significant scale. Collaborative environment emphasizing autonomy, experimentation, continuous learning, and engineering excellence.