Senior Backend Engineer (ML Platform)
Backend Engineer · Senior · Full Time
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Role
What you'll do.
Tennr is seeking a founding Senior ML Infrastructure Engineer to architect and scale cloud infrastructure powering AI-driven healthcare workflows. In this pivotal role, you'll design resilient systems for ML training, inference, and data pipelines while owning observability across the stack. This is an ideal opportunity for a distributed systems expert with 4+ years of production infrastructure experience who wants to grow into ML infrastructure in a fast-paced healthcare AI startup.
Responsibilities
- Infrastructure Architecture & Scaling: Architect, build, and scale the cloud infrastructure behind ML training, inference, and data pipelines. Design infrastructure decisions that support model deployment at scale while optimizing for cost efficiency and performance in AWS environments.
- Resilient System Design: Design and implement resilient systems for model deployment, evaluation, and monitoring that maintain reliability as traffic grows. Implement circuit breakers, graceful degradation, and fault tolerance patterns across the ML infrastructure stack.
- Observability Implementation: Own observability across the entire stack including logging, metrics, tracing, and alerting. Build comprehensive monitoring dashboards and alerting rules to detect and respond to production issues proactively.
- Production Troubleshooting & Optimization: Troubleshoot production incidents and continuously improve system performance and resource efficiency. Lead post-incident reviews and implement improvements to prevent recurring issues in distributed ML systems.
- Cross-functional Collaboration: Collaborate with ML engineers, backend engineers, and product teams to integrate machine learning models cleanly with data pipelines and customer-facing products. Define and document APIs and integration patterns for ML model deployment.
Qualifications
What we look for.
Technical
Distributed Systems Expertise
Demonstrated mastery in designing and implementing distributed systems with deep understanding of consistency, availability, and partition tolerance tradeoffs. Experience managing system state across multiple services.
Python Proficiency
Strong production-grade Python development skills for building infrastructure tools, data pipelines, and backend services. Familiarity with async frameworks and performance optimization techniques.
TypeScript Proficiency
Solid TypeScript skills for backend development, particularly in Node.js environments. Experience with type safety and scalable application architecture in JavaScript ecosystems.
AWS Platform Mastery
Hands-on, production-level experience with AWS services including EC2, RDS, S3, SQS, Lambda, VPC, and infrastructure-as-code tools. Deep understanding of AWS cost optimization and architectural patterns.
PostgreSQL Database Administration
Strong experience designing and maintaining PostgreSQL databases at scale including query optimization, indexing strategies, replication, and backup management. Understanding of ACID properties and transaction isolation.
Observability & Monitoring
Expert-level knowledge of logging, metrics, distributed tracing, and alerting. Experience with tools like Datadog, New Relic, or similar platforms. Strong understanding of SLOs, SLIs, and error budgeting.
Production Incident Management
Proven track record managing and resolving production incidents. Strong capability in root cause analysis, incident documentation, and implementing preventative measures in complex systems.
Education
Computer Science or Related Field
Bachelor's degree in Computer Science, Software Engineering, or equivalent field preferred. Equivalent professional experience demonstrating mastery of computer science fundamentals will be considered.
Experience
Infrastructure Engineering
Minimum 4+ years building and scaling production infrastructure in distributed systems, cloud platforms, or data engineering roles. Experience architecting systems that serve millions of requests or process large data volumes.
Backend Software Engineering
Solid foundation in backend software engineering fundamentals with experience designing scalable, maintainable APIs and services. Understanding of microservices architecture, containerization, and deployment patterns.
Startup Mentality
Comfortable navigating ambiguity in fast-paced startup environments with demonstrated high ownership. Track record of driving projects from concept through production deployment with minimal supervision.
ML Infrastructure Growth Path
Demonstrated interest in growing into ML infrastructure and MLOps domains. While prior ML ops or infrastructure experience is not required, clear interest in expanding technical skills into machine learning systems is essential.
Skills
Required
Python
Production-grade Python development for infrastructure and backend services
TypeScript/Node.js
Backend development with TypeScript in Node.js environments
AWS
Hands-on AWS architecture and cloud platform expertise
PostgreSQL
Database design, optimization, and administration at scale
Distributed Systems Design
System architecture for scalability, reliability, and performance
Observability Tools
Logging, metrics, tracing, and alerting implementation and optimization
Infrastructure as Code
Terraform, CloudFormation, or similar tools for declarative infrastructure management
Preferred
Kubernetes (k8s)
Nice to haveContainer orchestration and Kubernetes cluster management experience
Inference Engines
Nice to haveExperience with vLLM, SGLang, TensorRT, or similar ML inference engines
ML Frameworks
Nice to haveFamiliarity with PyTorch, TensorFlow, or other machine learning frameworks
Data Pipeline Tools
Nice to haveExperience with Apache Airflow, dbt, or similar data orchestration platforms
Healthcare Domain Knowledge
Nice to haveUnderstanding of healthcare workflows, insurance processes, or medical technology
Vector Databases
Nice to haveExperience with Pinecone, Weaviate, Qdrant, or similar vector database systems
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 200,000 – 230,000
Equity·Stock options
Benefits
Unlimited PTO
Flexible time off policy enabling you to manage work-life balance while maintaining productivity. Trust-based approach to vacation and professional development time.
100% Paid Employee Health Benefits
Comprehensive health insurance coverage including medical, dental, and vision plans fully funded by Tennr with zero employee premium contributions.
Employer-Funded 401(k) Match
Competitive retirement savings program with employer matching contributions to help you build long-term financial security.
Competitive Parental Leave
Generous parental leave policy supporting work-life transitions and family planning for all employees.
Modern Office Environment
Beautiful new office space at 345 Hudson Street in Hudson Square, New York featuring collaborative spaces and modern amenities for 4 days/week in-office presence.
Free Lunch & Snacks
Complimentary daily lunch and fully stocked snack pantry supporting nutrition and team bonding during working hours.
Equity Participation
Stock options allowing founding team members to participate in company growth and share in long-term value creation.
Process
Interview steps.
- 01
Technical Screening Call
Initial 30-minute conversation with a recruiter to discuss your background, experience with distributed systems, and understanding of ML infrastructure challenges. Confirms alignment on role expectations and compensation.
- 02
System Design Interview
1-hour technical interview focusing on infrastructure architecture decisions. You'll design systems for ML training pipelines, inference serving, or data processing at scale. Emphasis on tradeoffs, scalability, and production reliability.
- 03
Backend Engineering Deep Dive
1-hour coding interview assessing Python and TypeScript proficiency. Problems focus on distributed systems concepts, database optimization, or infrastructure automation. Discussion of production considerations and edge cases.
- 04
Infrastructure & Observability Discussion
45-minute conversation with an ML infrastructure engineer covering monitoring strategies, incident response patterns, and your approach to building reliable systems. Discussion of past production incidents and resolution approaches.
- 05
Leadership & Culture Fit
30-minute meeting with a senior engineer or founder exploring your collaboration style, comfort with ambiguity in startups, and interest in growing into ML infrastructure domains. Assessment of alignment with Tennr's high-ownership culture.
- 06
Offer & Negotiation
Comprehensive compensation discussion including salary, equity details, and benefits. Opportunity to ask questions about technical roadmap, team structure, and long-term growth opportunities.
Full posting
Original listing.
Company Description
Today, when you go to your doctor and get referred to a specialist, your doctor sends out a referral and tells you, “They’ll be in touch soon.” So you wait. And wait. Sometimes days, weeks, or even months. Why? Because too often providers are overwhelmed with the painstakingly tedious work required to get paid by insurance companies. Powered by proprietary models, Tennr handles the complex paperwork that gets patients through the door and providers paid, helping operators get patients the right care, at the right time, in the right setting.
Role Description
We're looking for a founding Sr. ML Infrastructure Engineer with a strong background in distributed systems and building pipelines that scale. In this role, you'll own the infrastructure that powers Tennr's AI-driven healthcare platform - the training, inference, and data pipelines that let our models handle growing traffic and an expanding product surface.
Our ML team builds in-house, proprietary VLMs, LLMs, and other models purpose-built for hard problems in healthcare. You don't need deep ML experience to thrive here - what matters is a strong system design foundation and the interest to grow into the ML-side. If you think in systems, care about reliability, and want to expand into ML infrastructure, this is a rare chance to build foundational systems from the ground up.
Responsibilities
Architect, build, and scale the cloud infrastructure behind our ML training, inference, and data pipelines.
Design resilient systems for model deployment, evaluation, and monitoring that stay reliable as traffic grows.
Own observability across the stack - logging, metrics, tracing, and alerting.
Troubleshoot production issues and continuously improve performance and efficiency.
Collaborate with ML engineers, backend engineers, and cross-functional teams to integrate models cleanly with data pipelines and products.
Candidate Qualifications
4+ years building and scaling infrastructure in production-distributed systems, cloud platforms, or data engineering.
Strong backend software engineering fundamentals, with proficiency in Python and TypeScript.
Hands-on experience with AWS, and PostgreSQL
Solid grasp of observability, reliability, and production incident response.
Comfortable with ambiguity and high ownership; you move fast and drive projects from idea to production in a startup environment.
Interested in growing into ML infrastructure - prior ML ops/infra experience is not required.
Nice to have: exposure to inference engines (vLLM, SGLang, TensorRT), or k8s.
Why Tennr?
Drive Impact: one of our company values is Cowboy, meaning you set the pace. You won’t just talk about things, you’ll get them done. And feel the impact.
Develop Operational Expertise: learn the inner workings of scaling systems, tools, and infrastructure
Innovate with Purpose: we’re not just doing this for fun (although we do have a lot of fun). At Tennr, you’ll join a high-caliber team maniacally focused on reducing patient delays across the U.S. healthcare system.
Build Relationships: collaborate and connect with like-minded, driven individuals in our Hudson Square office 4 days/week
Free lunch! Plus a pantry full of snacks.
Benefits
Beautiful new office at 345 Hudson Street
Unlimited PTO
100% paid employee health benefit options
Employer-funded 401(k) match
Competitive parental leave
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