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Employer: mZero

Full-time, in-office in Emeryville, California. Reports directly to the CEO. $120,000–$180,000 annual base salary plus equity. Required international and site travel.

Take field-successful vector-control compounds through WHO engagement, national regulatory approvals, market access and partnerships to commercial use.

Key Responsibilities

• Lead WHO engagement, prequalification and country submissions.
• Close remaining evidence and product-readiness gaps.
• Resolve manufacturing, supply, distribution and procurement barriers.
• Own commercialization plans, milestones, costs and partnerships.

Qualifications

• Personally advanced a vector-control or public-health product from field evidence toward commercial use.
• Hands-on WHO and national regulatory experience.
• Strong malaria/vector-control knowledge and product-introduction experience in endemic settings.

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Employer: mZero

Full-time | In-office, Emeryville, California | Reports directly to the CEO

Annual base salary: $150,000–$180,000, plus equity.

You’ll translate our hit mZero repellent compounds from milligram-scale discoveries into pilot and manufacturing scale formulations. You’ll optimize solvent systems, work on controlled release systems, and ensure robust, reproducible processes for field trials and commercial production.

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

About mZero

mZero is a global scientific effort using mosquito behavior observations, laboratory experiments and machine learning to identify compounds worth testing. Its scientific work is intended to produce candidate compounds and field evidence that can support the development of useful vector-control products.

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Apply through mZero's application page

Employer: mZero

Full-time | In-office, Emeryville, California | Reports directly to the CEO

Annual base salary: $50,000–$75,000, plus equity.

mZero is seeking a Lab Technician to develop and implement behavioral assays that assess how insects respond to visual and olfactory stimuli. Your work will be critical in validating the efficacy of machine-learning-predicted compounds across multiple insect species.

Key Responsibilities

• Design and develop behavioral assays to assess insect response to predicted repellent compounds.
• Lead experiments to test compounds for broad-spectrum effectiveness.
• Analyze experimental data to optimize compound performance, ensuring both repellency and pleasant odor characteristics.
• Present findings to the leadership team and contribute to the ongoing development of novel insect repellents.

This is a full-time, in-office role based in Emeryville, California, reporting directly to the CEO.

Qualifications

• Experience in behavioral assays and insect behavior research.
• Excellent analytical skills.
• Strong work ethic.

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Apply through mZero's application page

Own the biological validity of mZero’s mosquito 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 mosquito approach, landing, departure, probing, and related host-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 mosquito husbandry, host-seeking 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, vector biology, neuroethology, chemical ecology, animal behavior, or a related field
• Substantial experience designing and interpreting insect behavioral assays, preferably with hematophagous 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 Aedes or Anopheles host-seeking behavior and repellent 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

Build the computational chemistry layer that connects molecular structure, physicochemical properties, formulation context, and observed mosquito behavior. Your work should help mZero 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 mosquito landing 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

Employer: mZero

Full-time | In-office, Emeryville, California | Reports directly to the CEO

Annual base salary: $160,000–$260,000, plus equity.

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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Employer: mZero

Full-time | In-office, Emeryville, California | Reports directly to the CEO

Annual base salary: $160,000–$260,000, plus equity.

Build the reliable systems that carry mZero 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 mZero 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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Turn raw assay video into precise, reviewable measurements of what mosquitoes 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 mosquitoes 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, arenas, mosquito 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

Build and operate the distributed laboratory network that makes mZero’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 standardized mZero mosquito-behavior protocol
• 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