AI/ML Engineer, Platform and Agents

Amide Technologies•Waltham, MA

17h•Onsite

About The Position

Amide Technologies is seeking an AI/ML Engineer to build the platform and agent layer connecting scientific data, internal models, and experimental decision-making. The role makes AI/ML capabilities usable by scientists through reliable data flows, model-ready datasets, internal agents, workflow orchestration, and interfaces to ELN/LIMS and laboratory systems. The engineer will work at Amide’s Waltham, Massachusetts site in a multidisciplinary environment with biology, chemistry, lab operations, data science, and software colleagues. The position will help turn fragmented experimental and computational workflows into an internal platform supporting prospective peptide design, assay interpretation, and program decisions. While vendors and contractors may support infrastructure operations, the scientific workflow logic, data model, and user-facing AI tools are expected to remain under internal ownership. The role also contributes to platform governance, documentation, reproducibility, and practical standards for using models in project decisions. This is a full-time position based at the company’s headquarters in Waltham, Massachusetts. Flexibility with regard to working hours is required. The role reports to the Chief Data Science Officer. Compensation includes salary, bonus, and equity. Amide states that it is an equal opportunity employer and is committed to an inclusive environment.

Requirements

Degree in Computer Science, Bioengineering, Data Science, or a related discipline.
Strong Python programming skills and experience building production-quality scientific software, data pipelines, APIs, or internal workflow tools.
Experience building AI agents, LLM-enabled scientific tools, workflow orchestration systems, and model-serving applications.
Familiarity with biological, chemical, assay, or laboratory data and the practical challenges of preparing such data for machine learning.
Good judgment about build-versus-buy tradeoffs, vendor management, and using outsourced infrastructure without losing scientific control.
Experience working in matrixed teams of scientists, engineers, and operations colleagues to meet project objectives.
Demonstrated problem-solving skills, strong organization, and clear communication across scientific and technical teams.
Experience integrating data from databases, APIs, ELN/LIMS systems, instrument outputs, and cloud data stores into reliable internal applications.

Nice To Haves

Experience with MLOps, model-serving patterns, feature stores, metadata tracking, or reproducible ML workflow infrastructure.
Experience building scientist-facing tools in pharma, diagnostics, synthetic biology, chemistry, or lab automation.
Ability to define a platform data model supporting synthesis, assay, sequence, structure, target, and program-level decision workflows.
Experience scaling a greenfield scientific platform from early prototypes to repeatable workflows used across multiple discovery programs.

Responsibilities

Build internal AI agents and workflow tools that help scientists query data, interpret model outputs, and make design decisions.
Integrate synthesis, assay, ELN/LIMS, and computational data into model-ready layers that support repeatable learning loops.
Orchestrate lab-in-the-loop modeling workflows connecting design, synthesis, testing, analysis, and next-round recommendations.
Partner with lab automation and scientific software colleagues to improve data capture, traceability, and handoffs between experimental and computational teams.
Build applications, APIs, and data services that expose ML capabilities to scientific users.
Work with outsourced infrastructure, MLOps, or data-engineering partners while maintaining internal ownership of scientific requirements and platform behavior.
Contribute to platform governance, documentation, reproducibility, and practical standards for model use in project decisions.