[Hiring] Applied AI Engineer @mpathic

Role Description

We are looking for an Applied AI Engineer to own how mpathic's production services are built on large language models (LLMs). You will sit on the platform engineering team, reporting to the CTO, and work closely with the AI/ML team, research, and Security and Compliance. The AI/ML team defines how a model should behave: which model, which prompt, which judge, and the evaluation bar it must meet. You make that behavior run safely, reliably, and cost-effectively in production at scale.

Our platform spans red teaming, dataset generation, annotation, and evaluation, much of it for healthcare and life sciences customers where quality and auditability matter. Your job is to build the paved road every team uses to ship LLM-powered features, so that every model and prompt change reaching a customer arrives through the same tested, observable, and auditable path.

This is a hands-on engineering role. You should be as comfortable designing a gateway or a CI release gate as you are sitting with a researcher to turn a working prototype into a production service. You will start as the owner of LLM and inference infrastructure in production, with room to shape how we build with AI as the team grows.

Qualifications

• 5+ years of backend software engineering, including multi-tenant SaaS services and APIs in production.
• Production experience building on commercial LLM APIs (for example Anthropic, OpenAI, AWS Bedrock, or Azure OpenAI), including prompt management, structured outputs, and tool use.
• Hands-on experience designing evaluation pipelines for LLM output and wiring them into CI/CD as release gates.
• Strong Python; NodeJS is a plus.
• AWS, containers (ECS or similar), and infrastructure as code (Pulumi, Terraform, or CDK).
• Observability tooling such as OpenTelemetry, Datadog, or similar, and the delivery toolchain (version control, feature flags, CI, and an issue tracker such as Linear).
• Working knowledge of application security: secrets management, access control, and handling of sensitive data.

Requirements

• Build a deep understanding of the platform, its LLM-backed services, and how a model or prompt change moves from prototype to production today.
• Inventory every place production code calls an LLM: which providers, models, features, and data each one uses.
• Publish the first approved list of models, providers, and features, with data retention, BAA coverage, and version pinning documented for each.
• Stand up the first version of the LLM gateway and move at least one production service onto it.
• Route all production LLM traffic through the gateway, with logging, cost attribution, failover, and PHI controls.
• Ship a release path for model and prompt changes: versioning, evaluation gates in CI, staged rollout, and rollback.
• Take AI/ML prototypes, with their eval harnesses and acceptance thresholds, into scalable production services.
• Deliver production monitoring for latency, cost, failures, and quality drift against the evals defined by the AI/ML team.
• Produce the audit evidence for LLM changes that SOC 2, ISO 27001, HIPAA, and FDA 21 CFR Part 11 require.

Benefits

• Enjoy building the platform other people build on, and measure success by how easily and safely the rest of the team ships.
• Treat a prompt change with the same rigor as a code change, and care deeply about reliability, reproducibility, and auditability.
• Understand modern AI evaluation concepts (LLM-as-judge, red teaming, regression evals) and how to turn them into automated release gates.
• Are comfortable with high-stakes, sensitive subject matter such as healthcare, safety, and wellbeing, and the data handling it demands.
• Can translate between researchers, clinicians, and engineers, turning a working prototype into a production service without losing what made it work.
• Communicate crisply through decision records, design docs, and diagrams, and can align people around a standard without relying on authority.
• Have shipped real LLM-powered products in production, shown through what you have built rather than years on a résumé.

Company Description

mpathic is building the future of trustworthy AI. Grounded in behavioral science and human-centered design, we provide the infrastructure for building AI systems that are safe, aligned, and emotionally intelligent.

We serve frontier AI labs, large enterprises building and deploying AI, and Life Sciences organizations. Our Life Sciences business brings clinician-led AI monitoring, safety detection, and expert clinical review to clinical trials, helping sponsors and CROs protect participants and data quality, with a strong presence in psychedelic and CNS research.