• Collaborate with cross-functional teams to design and build multi-agent workflows • Engage with clients to define requirements and oversee software development lifecycle • Implement evaluation pipelines to measure agent performance • Manage backend tasks and contribute to frontend development • Stay informed about advancements in AI

• Build and operate fast, reliable AI systems that power core platform features • Enable accurate, data-grounded recommendations and intelligent workflows with production-grade performance and reliability at scale • Contribute across multiple projects built on the B2B AI stack • Ensure deep understanding of client needs and alignment with team

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking an experienced Staff Engineer to architect, deliver, and scale advanced AI/ML systems that directly drive business value. This role is ideal for a technical leader who thrives at the intersection of machine learning, architecture, and cloud engineering. - Architect and implement complex AI/ML systems that align with business and product objectives within the AWS cloud. - Serve as a subject matter expert on application engineering systems integrating with AI/ML components. - Drive large scale AI/ML project execution and foster cross functional collaboration between data science, ML engineering, and product teams. - Define and champion best practices for engineering, including code quality, system reliability, and operational excellence. - Mentor ML engineers to elevate team capabilities and maintain technical excellence. Qualifications - Authorized to work in the United States and not require work authorization sponsorship by our company for this position now or in the future. - 7+ years of experience in cloud native engineering development, with at least 3 years in a senior or staff level technical role. - Demonstrated experience of shipping/delivering large scale ML engineering projects. - Proven expertise in architecting and scaling production grade AI/ML systems, including model serving, training pipelines, and observability. - Deep understanding of distributed systems, data pipelines, and cloud native architectures (e.g., AWS, Kubernetes, Terraform, CI/CD frameworks). - Strong proficiency in Python and familiarity with frameworks such as TensorFlow, PyTorch, or similar ML libraries. - Demonstrated ability to drive technical alignment and execution across multiple teams or departments. - Experience applying ethical AI principles, including fairness, transparency, and privacy compliance, to real world projects. - Strong communication and leadership skills for influencing and guiding technical teams. - Advanced degree in Computer Science, Engineering, or a related field preferred; equivalent experience considered. Requirements - Tech Stack: Python, AWS (SageMaker, ECS, Lambda, S3, Fargate, etc.), Terraform, CI/CD frameworks. Benefits - Competitive compensation ranging from $170,000 - $200,000 per year PLUS quarterly bonus. - Medical, dental, vision, 401k program, and more. - Paid time off, including seven (7) federal holidays plus two (2) flex holidays for DEI. - Joining a rapidly growing organization. Company Description Trajector is where purpose meets progress. We specialize in medical evidence services that become the compass our clients rely on while navigating the intricate terrain of disability benefits. Our calling is clear: to make a real difference, infuse passion, and enhance the quality of life for the disabled community. As part of our global community, you'll join a team of over 1,800 dedicated individuals, each contributing their unique talents to streamline the path to benefits.

• Collaborate with the Head of AI, product managers, and engineers • Engineer and deploy ML models into real-time systems • Build robust, low-latency ML pipelines using Python, C++, and SQL • Apply adversarial learning and behavior modeling • Design and implement workflows that enhance data quality • Monitor, evaluate, and optimize deployed models • Present results and trade-offs to stakeholders • Contribute to Blackwall’s AI engineering practices by mentoring peers