Sr. Machine Learning Engineer

Team: IT

Location: US

Commitment: Full-time

Workplace Type: remote

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.