Fundamental

Machine Learning Engineer

• Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
• Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.
• Develop scalable inference architectures optimized, with ultra-low latency and high throughput
• Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities
• Develop logging, alerting, and monitoring solutions to track model development, and reliability
• Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency
• Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

MLOps Engineer

Data Scientist

• Research and develop data science methods that improve NEXUS predictive performance across diverse enterprise datasets, industries, and prediction task types
• Design and implement robust, production-quality Python components with a strong focus on correctness, generality, and reusability
• Deeply understand the characteristics of real-world enterprise data and develop strategies that help NEXUS handle them reliably
• Run rigorous experiments to measure the impact of new approaches, design meaningful benchmarks, and use results to guide prioritization
• Work across a wide variety of structured data problems - including but not limited to classification, regression, ranking, and forecasting
• Collaborate closely with the Engineering and Research teams to develop a deep understanding of NEXUS model behavior and use that knowledge to inform your work
• Work with Applied AI Engineers to validate approaches on real customer datasets and translate findings into product capabilities
• Contribute to technical documentation and internal best practices, helping the broader team apply new capabilities correctly and confidently

Data Scientist – Extensions

Backend Engineer

• Own the reliability, performance, and scalability of Extensions capabilities end to end
• Design and build distributed workflows that are efficient, reproducible, and observable in production
• Collaborate closely with Data Scientists on the team to deeply understand requirements and translate them into engineering decisions
• Identify and resolve performance bottlenecks, ensuring Extensions capabilities meet the latency and throughput demands of enterprise deployments
• Contribute to architectural decisions around how Extensions integrates with the broader NEXUS backend
• Write clean, well-tested Python code and hold a high bar for code quality across the team
• Contribute to internal engineering documentation and help establish best practices for backend development within the team

Backend Engineer – Extensions

Solutions Engineer

• Lead technical discovery to pinpoint high-feasibility, high-impact use cases where NEXUS demonstrates clear ROI
• Partner with Sales to drive the technical win by scoping and building "Art of the Possible" demos, running competitive benchmarks, and prototyping solutions on customer data
• Own the technical strategy for customers, maintaining deep relationships with senior stakeholders and defining the long-term strategy for adoption and expansion
• Lead the end-to-end delivery project, coordinating Forward Deployed Engineers and customer teams to resolve technical blockers and drive alignment across IT, Governance, and Business stakeholders
• Design robust solution architectures and integration patterns that ensure production-grade performance in complex enterprise environments (On-prem, VPC, Legacy systems)
• Translate patterns from the field into high-signal engineering tickets for Product and Research to inform the roadmap and model evolution
• Codify winning deployment strategies into reusable assets and reference architectures to accelerate the entire Solutions Architect team.
• Travel 50% of the time

Solutions Architect

Technical Program Manager

• Toolset standardization. You will consolidate tools to as few systems of record as possible, with a single source of truth for SDLC. Day-one priority.
• SDLC end-to-end. Own Linear, partner to set real deadlines, hold the line on them, and give leadership an accurate six-month forward view of capacity across Applied AI, Engineering and Research. Design automated development workflows that can keep pace with AI-accelerated engineering, ensuring that compliance, security, and privacy reviews are inherently baked into the system.
• The FDE-to-Engineering handoff. Formalize the path that pulls FDE-built extensions into the core platform. Determine when extensions get productized, when they get deprecated, and when functionality folds into the core model.
• Cross-team coordination. Run the operating rhythm that connects Applied AI, Engineering, and Research. Kill the downtime on blocked dependencies across SF, Europe, and Israel.
• API & Technical Specification Alignment. Act as the facilitator for cross-team technical definitions. Prevent delivery bottlenecks by ensuring early agreement on API signatures, terminologies, and system design before Engineering is asked to build.
• Delivery accountability. When a capability is built but stuck before launch, you are the person who gets it across the line.
• Operational reviews. Establish and run a weekly or biweekly review with engineering leadership that surfaces what's on track, what's blocked, and what needs an executive decision.
• Collaboration. Work across departments (Marketing, Sales, etc.) for collaboration, communication and coordination.

MLOps Engineer

Data Scientist – Extensions

Backend Engineer – Extensions

Solutions Architect

Senior Technical Program Manager