Careers

Remote contract · Commission-based, uncapped · Path to a full-time role

You will be one of our first commercial hires. You find labs and instrument makers, get them on a pilot of our AI agents, close the deal, then own the account: onboarding, setting up agent workflows on their data, and making sure they renew.

What you will do

• Build target lists of regulated testing labs, QC labs, contract labs and lab instrument makers
• Run outbound on LinkedIn, email and phone, and book meetings with lab managers, QA leads and service directors
• Run discovery and demos, and turn lab pain (re-tests, audit findings, slow reporting, service backlogs) into a clear ROI case
• Scope and run 30 to 60 day pilots with our engineering team
• Close contracts, then onboard the customer and stay their main contact
• Help customers connect their data systems (LIMS, databases, spreadsheets) and tune agent workflows to how they work
• Bring product feedback back to the team and write short case studies

You might be a fit if you

• Have a degree in engineering, chemistry, data science, or similar, or have worked in a lab
• Have 0 to 4 years of experience in sales, SDR/BDR work, lab work, field service or technical support
• Are comfortable with spreadsheets and basic SQL or Python, or keen to learn fast
• Enjoy cold outreach, can hear no and keep going, and can show proof of it
• Write short, clear emails and manage your own pipeline

Bonus points

• Knowledge of lab operations, LIMS, QA/QC or ISO 17025
• Contacts at testing labs or lab equipment companies
• Experience with HubSpot, Apollo, Clay or LinkedIn Sales Navigator

Pay

Commission-based and uncapped. You earn on booked meetings, paid pilots, closed deals, renewals and implementation work; details are shared in the interview. Top performers are offered a path to a full-time role with a base salary.

Remote · 3 to 6 months · Part-time or full-time · Path to a full-time role

You will build the agent loops at the heart of Trucision: AI agents that work on top of lab management and data systems. They plan a task, use tools, check their own work and hand off to a person when they are unsure.

What you will do

• Build tool-using agents that read results, QC records and service logs, then triage issues, open and route tickets, and draft reports
• Build retrieval over structured and unstructured lab data (SQL plus vector search) so every answer cites its source
• Build evaluation harnesses: test sets, grounding checks, and rules for when the agent should say it does not know
• Add guardrails, human approval steps, logging and audit trails suited to regulated environments
• Ship features end to end with the team and demo them to customers

You might be a fit if you

• Are a student or recent graduate in computer science, data science, engineering or a related field
• Write solid Python and are comfortable with SQL
• Have built something with LLM APIs or open models, such as an agent, a RAG app or a tool-calling workflow
• Care about measuring whether it works, not just demoing it
• Can work at least 4 hours a day overlapping US Central time

Bonus points

• Experience with agent frameworks (LangGraph or similar), MCP, or local models
• Experience with evaluation, observability or prompt optimization
• Front-end skills (TypeScript, React) to build agent UIs

Remote contract · Part-time or full-time · Path to a full-time role

You will build the models that tell our agents how likely an instrument or process is to drift or fail, and how sure we are. Your models turn noisy lab data with few failure labels into risk scores, onset dates and clear explanations that people can act on.

What you will do

• Build hierarchical Bayesian models that learn across a fleet of similar instruments
• Build time-to-event and survival models (Weibull, Cox, competing risks) and remaining-useful-life estimates with honest uncertainty
• Detect drift and change points, and date when behavior changed
• Calibrate models and use methods like conformal prediction so outputs come with reliable confidence
• Work with sparse labels, pseudo-labels and expert feedback
• Turn model output into decisions (what to service, when, and why) and work with the agent team so the reasoning is explainable

You might be a fit if you

• Have an MS or PhD in statistics, reliability engineering, operations research, applied math, or machine learning, or equivalent experience
• Are strong in Python and at least one probabilistic tool (PyMC, Stan, NumPyro or similar)
• Have shipped or published work on reliability, survival analysis, anomaly detection or prognostics
• Can explain uncertainty to non-statisticians in plain words

Bonus points

• Experience with public prognostics datasets (for example NASA C-MAPSS or bearing run-to-failure data)
• Background in statistical process control or quality engineering
• Experience in regulated environments (ISO 17025, GMP or similar)

Remote contract, part-time or full-time · Or advisor, paid hourly · Path to a full-time role

You have spent years maintaining, calibrating, troubleshooting and repairing analytical instruments. We need your judgment built into our AI agents and machine learning models. You will teach the agents how an expert thinks, check that what they recommend is right, and help customers trust the results.

What you will do

• Map failure modes for common instrument types: what fails, the early warning signs in results and QC data, likely causes and fixes
• Turn your troubleshooting steps into decision trees and checklists our agents can follow
• Review flagged runs and agent recommendations, and label them right or wrong with the reason, so our models learn
• Define what healthy looks like for calibration, QC checks, system suitability and preventive maintenance
• Help design service and compliance report templates that labs and auditors accept
• Join customer calls as the expert who speaks the lab's language

You might be a fit if you

• Have 5+ years in field service, instrument service engineering, applications support or lab instrument maintenance
• Know at least two of: GC or GC-MS, HPLC or UHPLC, LC-MS, ICP-OES or ICP-MS, AAS, FTIR or UV-Vis, titrators, or similar lab instruments
• Have hands-on experience with preventive maintenance, calibration, qualification (IQ/OQ/PQ) and repair
• Can explain why an instrument is failing, not just which part to swap
• Write clear, step-by-step notes

Bonus points

• Factory service training from a major instrument manufacturer
• Experience in ISO 17025, GMP or GLP labs
• Experience with LIMS or chromatography data systems
• Some comfort with spreadsheets or basic data analysis; we will teach you the AI side

Two ways to work with us

• Contract: part-time or full-time, paid monthly or hourly, with a path to a full-time role
• Advisor: a few hours a month reviewing agent output and joining expert sessions, paid hourly. Senior and retired service engineers are very welcome.

Remote video call · 45 minutes · $75–$150 or gift card equivalent, depending on experience

We're building agentic AI for lab management and quality data. We want to learn from people who live with instrument downtime, QC failures, audits and service scheduling every day.

Who we want to talk to

• Lab managers and lab directors
• QA/QC leads and quality managers
• Instrument service engineers and field service technicians
• Lab analysts who own instrument maintenance or calibration records

What we'll ask about

• How you track maintenance, calibration and instrument history today
• What causes the most downtime, rework or audit findings
• Where time goes on records, reports and approvals
• What a useful AI assistant would and would not be allowed to do

Remote · Contract with option to transition · US, Canada, Latin America, India

We hire for talent and drive before titles. If none of our open roles fits exactly, but you could make Trucision better, tell us how.

Areas we care about

• Agentic workflows and LLM tool use for lab operations, QA and compliance
• ML on lab management data: reliability, forecasting, anomaly detection
• Data engineering: LIMS/ELN/CMMS integrations, data models, evaluation pipelines
• Full-stack product engineering and UX for scientific users
• Lab operations, quality and regulatory expertise (ISO 17025, GMP/GLP)
• Sales, customer success and implementation with labs

How to stand out

• Be specific about the problem you would own in your first 90 days.
• Show proof: something you built, shipped, sold or fixed.
• Short and concrete beats long and generic.