AI/ML Engineer, Platform and Agents

Amide Technologies•Waltham, MA

2d•Onsite

About The Position

Amide Technologies is a Massachusetts-based biotech company that designs therapeutic peptides and proteins which are difficult to obtain by conventional means, combining solid-phase peptide synthesis with biological expression. By using non-natural amino acids and artificial protein backbones, we design peptides that conventional chemistry and biology cannot reach. Because Amide makes compounds that have never been made before, the work regularly involves problems with no established playbook. We are seeking an AI/ML engineer to build the platform and agent layer that connects scientific data, internal models and experimental decision-making. This role will focus on making Amide’s AI/ML capability usable by scientists through reliable data flows, model-ready datasets, internal agents, workflow orchestration and interfaces to ELN/LIMS and laboratory systems. You will join a multidisciplinary environment at our Waltham, Massachusetts site and work closely with biology, chemistry, lab operations, data science and software colleagues. Role description: The qualified individual will have recognized expertise in AI/ML engineering, scientific software, data integration and applied automation. The candidate will help convert fragmented experimental and computational workflows into an internal platform that supports prospective peptide design, assay interpretation, and program decisions. Infrastructure operations may be supported by vendors and contractors, but the scientific workflow logic, data model, and user-facing AI tools must be owned and shaped internally.

Requirements

AI/ML Engineer, Scientific Software Engineer, or Platform Engineer with a degree in Computer Science, Bioengineering, Data Science or a related discipline.
Strong programming skills in Python 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 making such data usable for ML.
Good judgment about build-versus-buy tradeoffs, vendor management and how to use outsourced infrastructure without losing scientific control.
Experience working in matrixed teams of scientists, engineers and operations colleagues to meet project objectives.
Demonstrated effective 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, lab automation.
Ability to define a platform data model that supports 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 can support repeatable learning loops.
Orchestrate lab-in-the-loop modeling workflows that connect design, synthesis, testing, analysis and next-round recommendation.
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 strong internal ownership of scientific requirements and platform behavior.
Contribute to platform governance, documentation, reproducibility and practical standards for model use in project decisions.