Staff Applied Backend / ML Engineer
Lead the design and delivery of production-grade, AI-driven backend and ML systems, from whiteboard architecture to hardened production deployment.
Apply for this roleWe are seeking an Applied Backend / ML Engineer to lead the design and implementation of production-grade, AI-driven systems that power intelligent products at scale.
This is a deeply hands-on, senior technical role responsible for architecting distributed backend systems, designing and deploying machine learning pipelines, turning ambiguous business problems into robust, measurable solutions, and shipping hardened production systems rather than prototypes.
You will design high-performance APIs and data workflows, productionize ML models across training, evaluation, inference, and monitoring, and ensure reliability, security, and scalability across cloud environments. This role suits an engineer who thrives at the intersection of systems architecture, applied ML, and product impact, someone who can move from whiteboard design to production deployment without handoffs.
What you will do
Backend and distributed systems
- Architect and implement scalable backend services across REST, gRPC, async systems, and event-driven pipelines.
- Design data-intensive workflows and ETL and ELT systems.
- Build high-throughput, low-latency APIs.
- Own service reliability, observability, and performance tuning.
- Define API contracts and integration patterns across services.
Applied machine learning
- Design ML pipelines end to end, from data ingestion and feature engineering through training, evaluation, and deployment.
- Productionize models for batch and real-time inference.
- Build embedding pipelines, ranking systems, and classification and extraction models.
- Implement experimentation frameworks and offline and online evaluation loops.
- Establish model monitoring, including drift detection, performance tracking, and alerts.
Cloud and infrastructure
- Deploy services across cloud environments such as GCP, Azure, and AWS.
- Design CI/CD pipelines for ML and backend systems.
- Implement infrastructure-as-code practices.
- Ensure system security, compliance, and scalability.
Technical leadership
- Set engineering standards and architectural direction.
- Mentor engineers across backend and ML domains.
- Collaborate with product, data, and leadership teams.
- Break down ambiguous problems into measurable, iterative execution plans.
- Drive long-term AI and platform strategy.
What we are looking for
Required experience
- 6+ years of backend engineering experience.
- 2+ years of applied ML in production environments.
- Strong Python or Java expertise.
- Experience designing distributed systems at scale.
- Hands-on experience deploying ML models in production.
- Experience with SQL and NoSQL databases.
- Familiarity with event-driven architectures such as Kafka, Pub/Sub, and streaming systems.
ML and data expertise
- Feature engineering and data preprocessing.
- Embeddings, ranking, or similarity systems.
- Model evaluation frameworks.
- Model serving for batch and real-time.
- Experimentation and A/B testing systems.
- Production monitoring and observability.
Infrastructure and systems
- Cloud-native architecture across GCP, Azure, or AWS.
- Containerization with Docker and orchestration with Kubernetes.
- CI/CD pipelines.
- API performance optimization.
- Security best practices for distributed systems.
Nice to have
- Experience with large language models in production.
- Experience building data platforms or workflow orchestration systems.
- Experience with vector databases.
- Experience in B2B, industrial, or commerce platforms.
- Familiarity with infrastructure-as-code such as Terraform and Helm.
What success looks like
Within six months, you will have:
- Designed and shipped a production ML-backed service.
- Established reliability and observability standards.
- Improved the performance and cost-efficiency of core systems.
- Mentored engineers and elevated technical quality across the team.
Why join us
At atronous, we are building the intelligence layer for modern commerce, turning fragmented, broken product data into structured, AI-ready systems that power data activation, automation, and agentic commerce. Here you will:
- Build AI-native systems from first principles.
- Influence long-term platform architecture.
- Work on real-world, high-scale distributed systems.
- Own your work with high autonomy.
- Have a direct impact on product and company trajectory.
Interested?
Send a note and your resume to contact@atronous.ai and we will take it from there.
Apply for this role