Software Engineer Algorithm Staff

IDEXX•Westbrook, ME

About The Position

The Staff ML Engineer, AI Enablement is a newly created senior technical role within the AI Enablement team. This is a greenfield opportunity: there is no inherited platform or playbook; this engineer helps define the standards, patterns, and tooling that teams will follow as they integrate agentic AI into their existing workflows and projects. The role is divided equally between two modes: applied technical exploration that shapes the team’s forward direction, and hands-on enablement that steps in where teams need it most. On the exploration side, you’ll lead applied technical work to identify emerging frameworks, patterns, and tooling, with a primary focus on agentic AI systems, multi-agent orchestration, and LLM-powered workflows. The goal is to accelerate innovation and existing project work by introducing agentic patterns that let practitioners design and train algorithms without friction, while maintaining a consistent experience and ensuring that security guardrails and controls are applied uniformly. You’ll translate findings into concrete prototypes, reference implementations, and usage patterns teams can rely on, with a clear near-term target: agentic guardrails, tooling, and adoption across all teams within the first year. Exploration priorities are shaped in close partnership with project architects, ensuring the work stays anchored to real platform needs. Outputs feed directly into architectural decision-making, giving architects the evidence they need to make confident calls on standards and investment. On the enablement side, you’ll embed directly with teams as high-complexity problems arise, contributing code, shaping solutions, and providing implementation-level depth where team bandwidth or specialization creates a gap. Engagements are prioritized in coordination with project leaders and conducted within guardrails set by project architects. Where embedded work surfaces platform-level implications or architectural tradeoffs, you’ll escalate and collaborate rather than resolve unilaterally. Across both modes, you serve as a bridge between strategic technical direction and day-to-day execution, maintaining active working relationships with project architects, project leaders, and data science and ML engineering practitioners throughout. These relationships exist to keep engineering work anchored to real platform needs and to surface implications in both directions; coordination is not a standalone deliverable or the primary mode of value delivery in this role. This role is best suited for a senior engineer who is energized by context-switching, thrives with ambiguity, and brings the technical range to be equally credible in a design review, a pull request, and a prototyping spike.

Requirements

8+ years of experience in machine learning engineering or software engineering, with a track record of building and delivering production ML systems; data science backgrounds will be considered where paired with demonstrated depth in production engineering and software delivery
Proven experience operating at the staff, principal, or tech lead level across multiple teams or product areas
Demonstrated ability to work across the full ML lifecycle, from exploratory prototyping through to production-quality implementation
Strong programming proficiency in Python and working knowledge of the modern ML and LLM ecosystem (e.g., PyTorch, HuggingFace, LangChain, LlamaIndex, or equivalent)
Hands-on experience designing or building agentic AI systems, including agent orchestration, tool use, retrieval-augmented generation (RAG), or multi-agent coordination
Experience contributing to or influencing architectural decisions in ML or data platform contexts
Able to thrive in ambiguous, self-directed environments with shifting priorities across multiple teams
Proven ability to collaborate with architects, engineering leads, and cross-functional stakeholders
Strong communication skills, with the ability to translate technical findings into clear recommendations that engineers, architects, and non-technical stakeholders can act on

Nice To Haves

Background in applied technical exploration, developer enablement, or platform engineering functions
Familiarity with MLOps platforms, feature stores, model registries, or ML infrastructure patterns
Experience with agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel) and evaluation strategies for non-deterministic AI systems
Experience operating within architectural governance models or center-of-excellence structures
Exposure to veterinary, healthcare, or life sciences domains

Responsibilities

Own the applied technical exploration agenda for agentic AI, identifying emerging frameworks, tooling, and integration patterns that can accelerate existing workstreams across DAICOE
Translate findings into prototypes, reference implementations, and usage patterns that teams can adopt directly
Define and drive adoption of agentic guardrails, tooling, and usage standards, giving practitioners the freedom to move quickly on algorithm design and training while ensuring a consistent experience and uniform security governance across teams
Develop reusable reference architectures for agentic systems, covering orchestration, tool use, memory, and evaluation strategies
Embed with MLE, data science, and data engineering teams during high-complexity engagements, contributing code and shaping solutions alongside team members
Maintain ongoing working relationships with data science and ML engineering practitioners, staying close enough to team-level work to identify enablement needs early and keep exploration priorities grounded in real problems
Identify platform-level implications and architectural tradeoffs surfaced during embedded work, and escalate to architects appropriately
Identify and surface cross-team alignment opportunities across data science, MLE, and data engineering, making shared patterns, tooling decisions, and integration points visible to architects and project leaders
Partner with project architects to keep exploration priorities anchored to strategic and platform needs
Coordinate with project leaders to ensure enablement engagements reflect the highest-priority team needs
Contribute to a culture of technical excellence across the AI Enablement team through shared learning, code review, and mentorship

Benefits

Opportunity for annual cash bonus
Health / Dental / Vision Benefits Day-One
5% matching 401k
Additional benefits including but not limited to financial support, pet insurance, mental health resources, volunteer paid days off, employee stock program, foundation donation matching, and much more

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