Computational Scientist, DNA and Protein engineering

Computational Scientist, DNA and Protein engineering ImmunoVec is building a nonviral platform for engineering cells inside the body. Instead of extracting a patient's cells, engineering them in a distant facility, and infusing them back into the patient, we deliver DNA to defined cell populations in vivo and pair it with synthetic, cell type specific promoters so the payload is expressed only in the intended cell. This approach offers two layers of control: the delivery vehicle decides where the DNA goes and the regulatory element decides where it is read. Our first proof of concept is an in vivo CAR therapy for autoimmune disease, supported by an award of up to $40.7 million from ARPA-H under the EMBODY program. We are about 20 people and work out of the California NanoSystems Institute at UCLA. 2. The role: design and analysis, informed by the biological realities of our screening system We are hiring a computational scientist to work as an integral part of our development process. You will design expression cassettes, which will be assessed in various forms by our molecular biology, cell culture, and in vivo teams (you will not be pipetting). You will use data and analysis produced by the other teams to identify the best features of one round of designs to maximize strength and specificity of expression of the next round. The iterative process is deliberate. The design space is large; we will alter protein expression and persistence by exploring the use of variable enhancers, promoter architecture, UTRs and introns, codon selection and CpG content, DNA format, detargeting, and protein co-factors. Cycle time is short and the number of variables we could change each round exceeds what we can test. We therefore need a designer who thinks creatively and understands how the experiments are run, which allows them to maximize the efficiency of each trial. A library that cannot be assembled cleanly, a construct that ignores nucleofection limits, or a readout that cannot resolve differences between cell types will waste substantial time and resources. This computational role will work entirely at the computer, but requires a strong engagement with other teams. You should be able to sit with a bench scientist, follow the protocol they are running, and see where your design will break it. Time at the bench in your own training is the fastest way to develop that instinct and counts strongly in your favor, although it is not an absolute requirement. The title of this role is flexible from Scientist to Principal Scientist, and ownership scales with it. At Scientist level you would take defined design and analysis workflows with direction from the CTO. At Senior or Principal level you would carry the specificity versus potency tradeoff across successive milestones and set the direction yourself. We calibrate to what you have personally done, not to years on a CV. 3. What you will own The scope below describes the role at Senior or Principal level. If you are ready to excel in some of these tasks, but not all, you may be hired at the Scientist level and could grow your title and responsibilities as you grow your abilities. 3.1 Cassette design. You own the design of cell type specific expression cassettes: which enhancer, which minimal promoter, what UTR and intron; you will optimize codon and CpG content and determine which detargeting or insulating elements are included. You decide what enters each build round and why. 3.2 Screens in primary human immune cells. You design individual reporters and libraries of reporters to be tested across human immune populations. After our wet lab teams execute the assembly, nucleofection, and flow cytometry, your job is to determine if the experiment satisfied its objectives. Determine what changes are needed to improve the design of the sequences or the design of the experiment. 3.3 Sequence to function modeling. You use and improve in house models that predict regulatory activity and cell type specificity from sequence, and close the loop by feeding screen results back into them. Familiarity with published models such as BPNet, Borzoi, or AlphaGenome helps. The job is to make predictions that hold in our cells and our assay, not to reproduce a benchmark. 3.4 Reading out in vivo studies. Our in vivo team runs humanized mouse studies weekly. You will use flow cytometry and sequencing data from those studies to determine how to optimize the next construct. That includes owning gating strategies that hold up under external technical review, and saying when a result does not support the conclusion someone in the room wants. 3.5 Persistence and vector architecture. You compare integration and episomal strategies on our own data, including transposase systems and episomal retention elements, and help determine which strategies to pursue. 3.6 Protein design for the persistence and integration layer. Persistence is not only a DNA problem. It runs on proteins: transposases, and engineered DNA binding proteins that tether a plasmid to chromatin through cell division. You design and triage those components. That means structure prediction, protein and DNA interface analysis, variant design for activity and specificity, and immunogenicity assessment of any protein sequence we would eventually put in a patient. Familiarity with tools such as RFdiffusion, ProteinMPNN, Boltz-2, or Chai-1 helps. 3.7 Written deliverables. We report against fixed milestone dates. You write the technical sections that go into milestone packages, patent disclosures, and partner reviews, and you present your own work at lab meetings. 4. What you will bring 4.1 Required PhD in molecular biology, immunology, genome engineering, computational biology, or a related field, plus postdoctoral or industry experience. Equivalent industry experience without the PhD is fine. Working knowledge of the wet lab process. You will not be running the experiments, but you should understand library assembly and cloning constraints, primary immune cell nucleofection, and flow panel design and gating well enough to design something executable and to recognize an artifact when you see one. Strong coding. Python or R at the level where you build and maintain your own pipelines rather than adapting someone else’s notebook. Comfort with amplicon, RNA-seq, ATAC-seq, single cell, and flow cytometry data. Experimental judgment. You can design an experiment with the right controls that returns a usable answer whether or not it works, and you can tell an artifact from a result. Willingness to state a conclusion plainly, defend it, and change it when the data change. 4.2 Strongly preferred MPRA, pooled reporter screens, or saturation mutagenesis. Regulatory genomics: enhancer and promoter biology, chromatin accessibility, transcription factor motif analysis. Sequence based deep learning applied to regulatory DNA. Expression cassette and vector optimization, including nonviral DNA formats, transposases, or episomal retention. NK or T cell biology, or CAR construct design. Protein design and structure prediction, particularly protein and DNA interface modeling. Immunogenicity and MHC-I epitope prediction, and deimmunization of therapeutic protein sequences. Transposase, recombinase, or integrase biology. Hands on bench experience at some point in your training, even if you no longer want to be at the bench. Working to fixed external milestones, in a federally funded program or an industry program with hard delivery dates. 5. How we work We are small and the calendar is set by external milestones, so the work is unusually concrete. You will know whether your construct worked within weeks, and so will everyone else. We would rather hear that a result is weak from you than from a reviewer. Bring the caveat with the figure. 6. Practicalities Location. Onsite at the California NanoSystems Institute at UCLA. Hybrid is workable for the computational portion of the work. Compensation. $130,000 to $180,000 base depending on level and experience, plus equity and benefits. #J-18808-Ljbffr ImmunoVec