Senior Software Engineer - Applied AI / Cloud

Job Title: Senior Applied AI Software Engineer (Software Engineer III)

Reports to: Engineering Manager

Direct Reports: None

Role ID: IT10000584C

FLSA Classification: Exempt

Location: Hybrid in Long Island City (Queens), NY*

• Must reside in one of the following states: NY, NJ, or CT

About Us Opentrons Labworks, Inc. is a disruptive life science company leveraging its integrated lab platform to supercharge the pace of innovation in research and healthcare. Through Opentrons Robotics, thousands of institutions are automating lab operations with flexible, easy-to-use liquid handling lab robots. With our own cutting edge R&D, biopharma and biotech at large can also benefit from our world-class genome-scale cell engineering solutions.

If you are looking for your next exciting opportunity, come join us! We provide excellent benefits, competitive compensation, equity packages, and opportunities for growth. Learn more on our Careers Website.

About The Role We’re looking for an Applied AI Engineer (SWE3) to help build AI-powered features as part of OpentronsAI, our platform that leverages Large Language Models to democratize laboratory automation. This role focuses on the internals of an AI-powered application: building the AWS-based microservices and asynchronous workflows that power protocol generation, integrating LLM calls and knowledge retrieval systems into production SaaS infrastructure, and making sure those systems are reliable and safe at scale for thousands of laboratories globally.

You’ll build the ML/AI systems layer that sits underneath the product, from parsing and understanding scientists’ natural-language input to the classifiers that route and score it and the embedding infrastructure that grounds model output in real protocol knowledge. Because correctness here isn’t optional, you’ll treat evaluation as a first-class part of the build, designing the tests and benchmarks that catch bad output before it ships, alongside the cost and safety instrumentation (token spend tracking, guardrails, validation) that keeps the system trustworthy at scale. You’ll bring the same engineering rigor to this layer that you’d bring to any production system: observability, cost control, and failure modes that fail safely rather than silently.

This is a hybrid position, requiring onsite presence at our Long Island City headquarters at least 3 days per week.

What You’ll Do

• Design, build, and operate the AWS-based microservices and asynchronous workflows that power protocol generation, including API design, service boundaries, message-driven communication, and integration points with the rest of the SaaS platform.
• Apply systems design principles to how AI components fit into the broader architecture: request routing, service-to-service communication, data flow, and scaling under production load.
• Build the ML/AI systems layer underneath the product: NLP pipelines for parsing scientist input, classifiers for routing and scoring requests, and embedding infrastructure that grounds model output in real protocol knowledge.
• Build and iterate on knowledge retrieval and retrieval-augmented generation (RAG) pipelines, including chunking, embedding/indexing, and retrieval tuning.
• Design and apply evaluation frameworks and rubrics to benchmark model and application output, and use them to drive iteration before issues reach production.
• Build the cost and safety instrumentation — token spend tracking, guardrails, and validation — that keeps AI services trustworthy and sustainable at scale.
• Instrument services for observability (logging, metrics, tracing) and build the monitoring needed to catch failures before customers do.
• Support features post-launch: on-call debugging, incident response, and iterating on the architecture based on real production usage.

Who We’re Looking For

Education

• Bachelor’s degree in Computer Science or similar field, or equivalent certifications and experience

Experience

• 5+ years of professional software engineering experience, including at least 3+ years spent building, deploying, and maintaining production AI/ML or LLM-powered applications.
• Experience shipping and supporting a SaaS product through a real launch, including the operational work that follows.
• Solid hands-on experience working within cloud microservice architectures (AWS strongly preferred) including deploying, scaling, and ope…