Master's degree in a relevant scientific or engineering field or equivalent practical experience.
Hands-on experience in molecular biology and mammalian cell culture.
Strong Python skills with experience in deep learning frameworks like PyTorch or JAX.
Experience in protein engineering, directed evolution, or RNA biology.
Demonstrated ownership of substantial projects in model training or laboratory automation.
Responsibilities
Design and execute protein evolution or RNA screening campaigns.
Develop and optimize cellular assays for measuring protein expression and signaling.
Generate and interpret sequencing and flow cytometry data.
Collaborate with computational scientists to enhance experimental designs.
Develop training pipelines for biological sequence models.
Build and evaluate training datasets from experimental screens.
Develop tools for translating experimental plans into traceable workflows.
Benefits
Health Care Plan (Medical, Dental & Vision)
Retirement Plan (401k, IRA)
Life Insurance (Basic, Voluntary & AD&D)
Paid Time Off (Vacation, Sick & Public Holidays)
Stock Option Plan
Full Job Description
Member of Technical Staff - Biological Engineering, ML, or Lab System
About the role
We are seeking hands-on scientists and engineers to develop this platform and advance our discovery programs. You will own a defined technical problem from initial design through implementation, validation, and iteration. Depending on your background, your work will focus on one of the following areas.
Candidates should bring depth in at least one of the following areas and be comfortable collaborating across disciplines.
1. Member of Technical Staff - Protein & RNA Engineering
Develop experimental systems that measure and improve how proteins and RNA function in human cells.
Responsibilities:
Design and execute protein evolution or RNA screening campaigns, defining functional objectives, experimental controls, and criteria for selecting promising variants.
Develop and optimize cellular assays that measure outcomes such as protein expression, binding, signaling, or cell fitness.
Generate and interpret sequencing, flow cytometry, or reporter assay data to connect sequence changes with biological function.
Investigate variability and failed experiments, improve reproducibility, and document protocols that other researchers can reliably execute.
Collaborate with computational scientists to select designs for testing and turn experimental results into datasets for model development.Relevant qualifications:
Hands-on experience in molecular biology and mammalian cell culture, with demonstrated ability to develop and troubleshoot assays.
Experience in at least one relevant area: protein engineering, directed evolution, CRISPR technologies, RNA biology, or pooled functional screening.
Familiarity with quantitative experimental analysis. Experience with primary human cells, sequencing library preparation, or high-throughput assays is a plus.
2. Member of Technical Staff - Machine Learning & Model Training
Train and improve protein and RNA models using Acelegen's experimental data. You will own model development from training data preparation and objective design through large-scale training, evaluation, and experimental validation.
Responsibilities:
Develop training pipelines for pretraining, continued pretraining, and fine-tuning biological sequence models.
Design training objectives that connect protein and RNA sequences to experimentally measured function, including the effects of interacting mutations and cellular context.
Build training datasets from experimental screens, with rigorous filtering, normalization, versioning, and train-validation-test splits.
Run systematic experiments across model architectures, training objectives, data mixtures, and hyperparameters; use ablations to identify what drives improvements.
Optimize training efficiency and stability through distributed training, mixed precision, memory optimization, profiling, and reliable checkpointing.
Evaluate models on unseen sequences and experimental conditions, and work with scientists to validate model-generated designs and incorporate new measurements into subsequent training rounds.Relevant qualifications:
Strong Python skills and hands-on experience implementing and training deep learning models in PyTorch, JAX, or a comparable framework.
Practical understanding of transformer architectures, optimization methods, loss functions, and training dynamics, including diagnosing unstable training and overfitting.
Experience building reproducible training pipelines and running GPU-based experiments, with systematic experiment tracking and model evaluation.
Demonstrated ownership of a substantial model training project, from dataset preparation through evaluation, in research or production.
Experience with distributed training, biological foundation models, sequence generation, or learning from experimental feedback is a plus.
3. Member of Technical Staff - AI Laboratory Systems & Automation
Build software and integrations that connect experimental planning, laboratory execution, and structured feedback.
Responsibilities:
Develop tools that translate experimental plans into traceable workflows and capture their execution.
Build pipelines that connect protocols, video, timestamps, annotations, and assay results.
Integrate AI assistants with laboratory software and instruments to support researchers during experiments.
Develop evaluations for recognizing laboratory actions, detecting procedural errors, and assessing whether recorded evidence supports an experimental conclusion.
Debug systems in the laboratory and improve their reliability, usability, and recovery from failures.Relevant qualifications:
Strong software engineering skills, including Python, APIs, data pipelines, testing, and debugging.
Experience building systems used in real workflows, with attention to logging, traceability, and failure handling.
Experience in at least one relevant area: laboratory automation, instrument integration, AI agents, computer vision, or multimodal systems. Familiarity with wet-lab workflows is a plus.
Requirements
A Master's degree in a relevant scientific or engineering field, or equivalent practical experience.
Evidence of completing substantial hands-on work, such as a validated assay, research project, model, or deployed system.
Ability to define milestones, investigate unexpected results, and communicate technical decisions clearly.
Careful documentation and a collaborative approach to working across biology and engineering.
