Kollectiv, Inc

The Role

As our Senior Backend Engineer, you’ll design and build the backend infrastructure that supports high-throughput, reliable, and secure AI agent workflows. You’ll work cross-functionally with ML engineers and data scientists to operationalize models at scale.

Key Responsibilities

• Architect and implement scalable APIs, microservices, and event-driven systems to power ML pipelines and agent orchestration
• Design efficient data modeling, ETL, and persistent storage suited for high-volume, real-time agent data 
• Optimize backend performance and ensure system stability under heavy concurrent load
• Collaborate closely with founders and ML teams to deliver end-to-end agent features
• Establish CI/CD pipelines, monitoring frameworks, and enforce backend security best practices
• Mentor junior engineers and contribute to team culture and best practices

Requirements

• 5+ years designing, implementing, and maintaining backend systems in Python
• Experience building distributed, scalable services with microservices or serverless architectures
• Strong grasp of relational and NoSQL databases, message queues (e.g., Kafka), and ETL patterns 
• Proven track record in API design (REST/gRPC), authentication, and reliable asynchronous processing
• Familiarity with containerization (Docker, Kubernetes) and observability tools (Prometheus, Grafana)

Nice to Haves

• Prior experience launching AI products or backend systems for ML/agent-driven applications
• Knowledge of LLM APIs, prompt pipelines, and agent orchestration
• Familiarity with cloud-native tools (AWS Lambda, S3, DynamoDB, GCP/Azure equivalents)
• Experience in startup or early-stage environments

Why You Should Join

• Competitive equity & pay - get in early and own what you build.
• Work closely with experienced founders with a proven startup track record.
• Move fast, ship fast - no corporate bureaucracy.
• Shape the AI revolution in healthcare - massive market, untapped potential

The Role

We’re looking for a Senior ML Research Engineer to lead the design, implementation, and optimization of large-scale models powering intelligent agents. Your role spans model architecture, shipping, and production monitoring.

Key Responsibilities

• Conduct cutting-edge research on agentic AI systems — exploring new methods in reasoning, memory, planning, and multi-agent collaboration — and translate findings into production-ready prototypes.
• Design, fine-tune, and evaluate large language models and neural networks for modular agent behavior
• Build robust ML pipelines (data ingestion, feature engineering, model training, serving, monitoring) 
• Implement evaluation frameworks, A/B testing, and performance metrics to quantify agent effectiveness
• Ensure reproducibility, traceability, and compliance across ML lifecycle
• Collaborate with backend engineers to define inference service SLAs and efficient real-time ML delivery
• Mentor junior ML teammates and establish team-level best practices

Requirements

• 5+ years as an ML Engineer or applied ML researcher with production model deployment experience
• Strong Python skills, experience with ML frameworks (TensorFlow, PyTorch) and LLM tooling (HuggingFace)
• Familiarity with data pipelines, SQL, cloud platforms (AWS)
• Solid understanding of model evaluation, A/B testing, and ML performance metrics
• Excellent collaboration skills with product, backend, and data teams

Nice to Haves

• Prior work with conversational agents, retrieval-augmented generation, or multi-model orchestration
• Experience with vector search stacks (e.g. Pinecone, FAISS)
• Reinforcement Learning experience
• Startup experience and ability to navigate ambiguity and shape technical direction

Why You Should Join

• Competitive equity & pay - get in early and own what you build.
• Work closely with experienced founders with a proven startup track record.
• Move fast, ship fast - no corporate bureaucracy.
• Shape the AI revolution in healthcare - massive market, untapped potential