Join
pZero’s team structure is being developed. These eight draft team areas adapt the M Zero reference and describe the work a crop-protection program could need. Openings, locations, and a recruiting contact have not been selected. Explore the local program preview while those decisions are made.
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Director of Formulation Chemistry
Lead the formulation work that turns promising repellent chemistry into stable, reproducible candidates that can move from behavioral assays toward real-world use.
Key responsibilities
- Lead bench-to-pilot scale-up of candidate compounds, focusing on solvent selection, solvent recovery, controlled release, and other critical scale-up variables
- Design and execute experiments to assess solubility, stability, and volatility
- Troubleshoot formulation issues and iterate quickly under real-world constraints
- Collaborate cross-functionally with predictive-modeling, behavioral assay, and computational chemist groups
Required qualifications
- M.S. or Ph.D. in Organic Chemistry, Chemical Engineering, or related field
- 3+ years’ hands-on scale-up or process chemistry experience
- Deep understanding of solvent properties, controlled release phenomena, and formulation principles
- Track record of developing robust, reproducible processes
- Strong data analysis, documentation, and troubleshooting skills
Desired attributes
- Entrepreneurial mindset: thrives in ambiguity and high-pressure environments
- Resilient problem-solver: tackles “hardest things” head-on
- Communicates clearly, honestly, and respectfully
- Collaborative spirit
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Lab Technician - Behavioral Assays
Help develop and run the insect behavior assays proposed for pZero’s training data. This is a hands-on role for someone who treats careful animal handling, consistent execution, and complete records as parts of the same scientific result.
Key responsibilities
- Run contact and non-contact behavioral assays for a defined insect and crop system, including matched solvent controls and validated reference treatments
- Prepare assay arenas, crop surfaces, test materials, insect cohorts, cameras, and controlled environmental conditions according to protocol
- Record assay videos for the protocol-defined duration and complete the associated metadata, filenames, quality checks, and traceable assay-day records
- Maintain insect colonies, host plants, assay equipment, chemical inventory, and contamination-control procedures
- Identify protocol deviations or unusual behavior quickly and work with scientists to determine whether a run should be repeated
Required qualifications
- B.S. or equivalent practical experience in biology, entomology, neuroscience, animal behavior, or a related laboratory field
- Hands-on experience with insectary work, behavioral experiments, or another high-precision biological workflow
- Ability to follow detailed protocols, pipette accurately, handle research chemicals safely, and maintain traceable records
- Comfort performing repetitive work without allowing repetition to reduce attention or data quality
- Clear written communication and reliable day-to-day organization
Desired attributes
- Experience working with crop-associated insects and controlled plant assays
- Experience with video-based behavior assays or automated data capture
- Strong instinct for noticing small procedural or behavioral anomalies
- Willingness to improve a protocol while preserving comparability across runs and laboratories
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Lead Entomologist - Behavioral Assay Development
Develop the biological validity of pZero’s proposed insect behavior platform. You will make the assay more informative without losing the standardization required to compare results across compounds, days, colonies, and labs.
Key responsibilities
- Lead development and validation of contact and non-contact assays for insect approach, landing, departure, feeding, and related crop-seeking behavior
- Define controls, replication, exclusion criteria, and endpoints such as cumulative landing-zone occupancy before experiments are run
- Design studies that separate compound effects from colony history, age, mating status, nutrition, temperature, humidity, airflow, and operator variation
- Set standards for insect husbandry, plant condition, behavioral readiness, assay quality, and inter-lab reproducibility
- Work with formulation, computer-vision, and machine-learning teams to turn biological observations into measurable features and better next experiments
- Train network labs, review deviations and anomalous results, and lead corrective experiments when the evidence is unclear
Required qualifications
- Ph.D. or equivalent research experience in entomology, crop protection, neuroethology, chemical ecology, animal behavior, or a related field
- Substantial experience designing and interpreting insect behavioral assays, preferably with crop-associated insects
- Strong command of experimental design, statistical analysis, controls, bias, and biological sources of variance
- Evidence of translating ambiguous observations into reproducible methods and defensible conclusions
- Ability to lead across laboratory science, engineering, and data teams
Desired attributes
- Direct experience with plant-feeding insects or beneficial insects, crop-seeking behavior, and behavioral compound testing
- Experience coordinating multi-site studies or harmonizing protocols across laboratories
- Familiarity with the gap between controlled laboratory efficacy and field performance
- Scientific judgment that is comfortable saying when a result is not yet interpretable
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Research Scientist - Computational Chemistry
Build the computational chemistry layer that connects molecular structure, physicochemical properties, formulation context, and observed insect behavior. Your work should help pZero choose more informative compounds to test, not merely explain results after the fact.
Key responsibilities
- Develop molecular representations and predictive models for compound effects on insect landing on crops and related behavioral endpoints
- Combine chemical structures and descriptors with formulation, dose, assay, environmental, and behavioral data
- Design prospective evaluations that measure whether model-ranked compounds outperform conventional selection approaches
- Quantify uncertainty, identify out-of-domain predictions, and propose experiments that distinguish competing chemical hypotheses
- Partner with formulation chemists and machine-learning researchers to recommend the next compound, dose, formulation, or controlled variant to test
- Build reproducible computational workflows with traceable structures, descriptors, model versions, and experimental outcomes
Required qualifications
- Ph.D. or equivalent research experience in computational chemistry, chemoinformatics, physical chemistry, medicinal chemistry, chemical engineering, or a related field
- Experience with molecular descriptors, similarity methods, QSAR, molecular machine learning, or graph-based models
- Strong Python skills and experience with tools such as RDKit or equivalent chemical-computing libraries
- Ability to design leakage-resistant evaluations and interpret model performance in chemical rather than purely statistical terms
- Clear scientific writing and close collaboration with experimental teams
Desired attributes
- Experience with active learning, Bayesian optimization, uncertainty calibration, or prospective molecular discovery
- Knowledge of volatility, solubility, controlled release, odorants, or insect-active small molecules
- Experience connecting computation to iterative wet-lab experiments
- Interest in building open, reusable scientific methods rather than a one-time screening model
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ML Researcher
Develop the learning methods that turn repeated assays into better scientific decisions. The central question is prospective: can a model use prior compound, assay, and behavior data to recommend an experiment that is more informative than the one scientists would otherwise run?
Key responsibilities
- Research models that combine molecular information, formulation and dose, assay metadata, video-derived behavior, and laboratory context
- Develop active-learning and sequential experiment-selection methods that balance predicted efficacy, uncertainty, novelty, and information value
- Define retrospective and prospective evaluations, including holdouts by chemical scaffold, laboratory, colony, and time
- Investigate which behavioral signals generalize across experiments and which reflect confounding, measurement noise, or laboratory-specific effects
- Translate model failures into new labels, assay variants, controls, or experiments that improve the next training cycle
- Communicate results with enough precision that experimental scientists can understand why a recommendation should or should not be trusted
Required qualifications
- Ph.D. or equivalent research record in machine learning, statistics, computational science, or a closely related field
- Demonstrated ability to formulate open-ended research questions, build strong baselines, and design evaluations that survive distribution shift
- Strong software skills in Python and a modern machine-learning framework
- Experience working with noisy, limited, multimodal, or experimentally generated datasets
- Ability to move between theory, implementation, and scientific interpretation
Desired attributes
- Experience with active learning, Bayesian optimization, reinforcement learning, causal inference, or scientific foundation models
- Experience in molecular discovery, biology, animal behavior, robotics, or another domain where models learn from physical experiments
- Track record of prospective validation rather than benchmark-only research
- Strong research taste and comfort abandoning an attractive idea when the evidence does not support it
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ML Engineer
Build the reliable systems that carry pZero data from an assay recording to a reproducible model, an evaluated prediction, and a usable recommendation for the next experiment.
Key responsibilities
- Own pipelines for ingesting, validating, versioning, and joining assay videos, metadata, compound records, model features, and experimental outcomes
- Build reproducible training and evaluation infrastructure with clear data lineage, model versioning, automated tests, and auditable outputs
- Turn research prototypes into dependable batch and online systems that can rank compounds and surface recommendations through pZero tools
- Monitor data quality, distribution shift, calibration, latency, cost, and failures as the number of labs and assays grows
- Design interfaces between computer vision, molecular models, active-learning systems, and the lab workflow
- Improve developer and researcher velocity without weakening scientific reproducibility or access controls
Required qualifications
- Strong production software engineering experience in Python and modern machine-learning or data systems
- Experience deploying and operating model-training, feature, evaluation, or inference pipelines in a cloud environment
- Fluency with testing, observability, data validation, version control, and reproducible computational workflows
- Ability to work with large video datasets and structured scientific data
- Ability to collaborate closely with researchers while making sound engineering tradeoffs
Desired attributes
- Experience with PyTorch, JAX, or TensorFlow and workflow-orchestration tools
- Experience on Google Cloud or with large-scale object-storage pipelines
- Familiarity with computer vision, molecular machine learning, active learning, or scientific data platforms
- Instinct for simple systems, explicit failure modes, and measurable reliability
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Computer Vision Engineer
Turn raw assay video into precise, reviewable measurements of what insects do over time. The work begins with detection and tracking, but the scientific outcome is a trustworthy behavioral record that can train and evaluate models.
Key responsibilities
- Develop and validate methods for detecting and tracking multiple insects in top-mounted behavioral-assay video
- Derive cumulative landing-zone occupancy, trajectories, spatial distribution, entry and exit rates, dwell time, and other interpretable behavioral features
- Build representative labeled datasets and error analyses across labs, cameras, lighting conditions, crop surfaces, insect densities, and occlusion patterns
- Quantify confidence and route uncertain or anomalous results to efficient human review rather than silently producing a score
- Design visual overlays and quality-control tools that let scientists inspect how each measurement was produced
- Work with entomologists and lab teams to improve camera placement, assay geometry, capture standards, and the behavior labels that matter scientifically
Required qualifications
- Strong experience with object detection, multi-object tracking, segmentation, pose or trajectory analysis, or related computer-vision methods
- Strong Python skills and experience with PyTorch, OpenCV, or equivalent tools
- Experience building evaluation sets and choosing metrics that reflect the downstream use of a vision system
- Ability to build efficient video-processing pipelines and debug failures at the frame and sequence level
- Clear communication with domain scientists and software engineers
Desired attributes
- Experience with small-object tracking, animal behavior, microscopy, or other visually difficult scientific video
- Experience with domain adaptation, weak supervision, active learning, or human-in-the-loop annotation
- Familiarity with camera calibration, experimental instrumentation, or cross-site capture standardization
- Interest in making scientific measurements interpretable and auditable
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Lab Network Lead
Build and operate the distributed laboratory network that makes pZero’s learning loop possible. Your job is to help independent labs produce comparable, timely, scientifically useful assay data without erasing the realities of how each lab works.
Key responsibilities
- Recruit, assess, onboard, and support laboratories capable of running the proposed pZero insect-and-crop assay framework
- Coordinate assay equipment, compound shipments, training, schedules, data submissions, troubleshooting, and follow-up experiments across the network
- Track capacity, turnaround time, protocol adherence, control performance, inter-lab agreement, missing data, and required repeat runs
- Build practical onboarding materials, support systems, escalation paths, and operating reviews that help labs succeed independently
- Preserve chain of custody, assay provenance, permissions, and clear ownership of every experimental record
- Bring recurring lab observations back to science, product, and operations teams so protocols and tools improve without breaking comparability
Required qualifications
- Significant experience in multi-site research operations, laboratory operations, scientific program management, or a comparable life-sciences network
- Demonstrated ability to turn a technical protocol into repeatable execution across organizations with different constraints
- Strong project management, written communication, data discipline, and relationship-building skills
- Comfort coordinating research materials, documentation, timelines, and quality issues across countries and time zones
- Willingness to travel to partner laboratories when remote support is not enough
Desired attributes
- Experience with entomology, vector biology, behavioral assays, or global health research
- Experience managing research consortia, contract laboratories, open-science collaborations, or distributed quality systems
- Familiarity with chemical-shipment coordination, laboratory safety, and cross-border research logistics
- Ability to be exacting about evidence while remaining a trusted partner to labs