Description

SystImmune is a leading and well-funded clinical-stage biopharmaceutical company located in Redmond, WA and Princeton, NJ. It specializes in developing innovative cancer treatments using its established drug development platforms, focusing on bi-specific, multi-specific antibodies, and antibody-drug conjugates (ADCs). SystImmune has multiple assets in various stages of clinical trials for solid tumor and hematologic indications. Alongside ongoing clinical trials. SystImmune has a robust preclinical pipeline of potential cancer therapeutics in the discover and IND-enabling stages, representing cutting-edge biologics development. We offer an opportunity for you to learn and grow while making significant contributions to the company's success.   SystImmune is seeking a highly skilled Senior or Principal Scientist with deep expertise in AI/ML and protein or antibody drug discovery. This role will drive the design and implementation of advanced machine learning models and data infrastructure to accelerate therapeutic discovery across our biologics pipeline. The ideal candidate combines strong technical fluency in machine learning, structural biology, and computational chemistry with a practical understanding of therapeutic design. You will lead efforts that directly inform candidate selection, antibody optimization, and molecular design through scalable, AI-driven workflows.  <h2 style="margin-top:13px;">Responsibilities</h2><ul style="margin-bottom:13px;"><li class="MsoListBullet" style="margin-bottom:13px;">Machine Learning Model Development</li></ul><ul style="margin-bottom:13px;list-style-type:circle;"><li class="MsoListBullet2CxSpFirst" style="margin-left:8px;">Design and fine-tune deep learning and LLM-based models (e.g., LLaMA 3.3, DiffDock, ProteinMPNN) for sequence–structure–activity prediction and optimization.</li><li class="MsoListBullet2CxSpLast" style="margin-left:8px;margin-bottom:13px;">Integrate antibody and protein-specific biological knowledge into model architectures and training strategies.</li></ul><ul style="margin-bottom:13px;"><li class="MsoListBullet" style="margin-bottom:13px;">Antibody and Protein Therapeutic Design</li></ul><ul style="margin-bottom:13px;list-style-type:circle;"><li class="MsoListBullet2CxSpFirst" style="margin-left:8px;">Apply ML to support antibody humanization, CDR optimization, stability prediction, developability filtering, and manufacturability assessment.</li><li class="MsoListBullet2CxSpLast" style="margin-left:8px;margin-bottom:13px;">Collaborate with discovery teams to deploy AI-driven workflows across antibody, multi-specific, and cyclic peptide programs.</li></ul><ul style="margin-bottom:13px;"><li class="MsoListBullet" style="margin-bottom:13px;">Data Integration & Pipeline Ownership</li></ul><ul style="margin-bottom:13px;list-style-type:circle;"><li class="MsoListBullet2CxSpFirst" style="margin-left:8px;">Build robust pipelines for aggregating and structuring internal R&D data (sequences, 3D structures, binding data, developability attributes) for ML modeling.</li><li class="MsoListBullet2CxSpLast" style="margin-left:8px;margin-bottom:13px;">Develop ETL systems and embedding workflows using LangChain, Milvus, or MariaDB Vector DB to support RAG-based knowledge retrieval and protein annotation.</li></ul><ul style="margin-bottom:13px;"><li class="MsoListBullet" style="margin-bottom:13px;">Scientific and Cross-Functional Leadership</li></ul><ul style="margin-bottom:13px;list-style-type:circle;"><li class="MsoListBullet2CxSpFirst" style="margin-left:8px;">Serve as the AI/ML technical lead on discovery programs, interfacing with computational biology, protein engineering, immunology, and bioinformatics teams.</li><li class="MsoListBullet2CxSpLast" style="margin-left:8px;margin-bottom:13px;">Mentor junior team members, guide experimental design informed by ML models, and ensure reproducibility and traceability for downstream applications.</li></ul><ul style="margin-bottom:13px;"><li class="MsoListBullet" style="margin-bottom:13px;">Computing & Productionization</li></ul><ul style="margin-bottom:13px;list-style-type:circle;"><li class="MsoListBullet2CxSpFirst" style="margin-left:8px;">Scale model training and inference across GPU/HPC environments using frameworks like Dask, Ray, MPI, or AWS.</li><li class="MsoListBullet2CxSpLast" style="margin-left:8px;margin-bottom:13px;">Ensure models and pipelines are integrated into scientific production environments (LIMS, R&D cloud platforms, etc.).</li></ul><h2 style="margin-top:13px;"> Requirements</h2><ul style="margin-bottom:13px;"><li class="MsoListBulletCxSpFirst">Ph.D. in Computational Biology, Computer Science, Bioinformatics, Structural Biology, or a related field</li><li class="MsoListBulletCxSpMiddle">5+ years of experience in AI/ML applied to protein or antibody drug discovery in an industry or translational research setting</li><li class="MsoListBulletCxSpMiddle">Proven track record of developing and applying ML models for antibody/protein optimization, structure-function modeling, or drug design</li><li class="MsoListBulletCxSpMiddle">Proficient in Python and deep learning frameworks such as PyTorch or TensorFlow</li><li class="MsoListBulletCxSpMiddle">Experience with LLMs, GNNs, and relevant tools for structure and sequence analysis (e.g., AlphaFold, Rosetta, DiffDock)</li><li class="MsoListBulletCxSpMiddle">Strong understanding of antibody engineering principles, therapeutic design challenges, and developability constraints</li><li class="MsoListBulletCxSpLast" style="margin-bottom:13px;">Demonstrated ability to work across disciplines and communicate complex ideas to cross-functional teams</li></ul><h2 style="margin-top:13px;"> Preferred Qualifications</h2><ul style="margin-bottom:13px;"><li class="MsoListBulletCxSpFirst">Experience building or leading AI/ML workflows embedded in therapeutic discovery pipelines</li><li class="MsoListBulletCxSpMiddle">Familiarity with AI-guided antibody design, cyclic peptides, or novel protein modalities</li><li class="MsoListBulletCxSpMiddle">Prior exposure to IND or regulatory-facing AI/data packages</li><li class="MsoListBulletCxSpLast" style="margin-bottom:13px;">Publication or speaking track record in ML for drug discovery or structural biology</li></ul><h3 style="margin-top:13px;"> Who This Role Is Not For</h3><ul style="margin-top:13px;"><li style="margin-top:13px;">This is not a general AI/ML engineering role. Candidates without prior experience applying machine learning to therapeutic discovery, protein modeling, or antibody design will not be considered.</li><li class="p4">We are specifically seeking scientists who have applied computational models in the biopharma or drug discovery setting, not those coming solely from academic ML, generic NLP, or unrelated AI fields.</li></ul> Compensation and Benefits: The expected base salary range for this position is $150,000 - $200,000 annually. Actual compensation will be based on a variety of factors, including but not limited to a candidate’s qualifications, experience, and skills. While most offers typically fall within the low to mid-point of the range, we may extend an offer toward the higher end for exceptional candidates whose background and expertise exceeds the requirements of the role.   SystImmune is a leading and well-funded biotech company with a bright future. We offer an opportunity for you to learn and grow while making significant contributions to the company’s success. SystImmune offers a comprehensive benefits package including: 100% paid employee premiums for medical/dental/vision, also STD, LTD, a 401(k) plan with a 50% company match of up to 3% and a vesting schedule of only 5 years, 15 PTO days per year, sick leave, plus 11 paid holidays and MORE.   We offer an opportunity for you to learn and grow while making significant contributions to the company’s success.   SystImmune is an Equal Opportunity Employer. We welcome diverse talent and encourage all qualified applicants to apply.

Senior/Principal Scientist – AI/ML Protein &amp; Antibody Drug Discovery

Description

SystImmune

Senior/Principal Scientist – AI/ML Protein & Antibody Drug Discovery