Senior Machine Learning Engineer Omics and Graph Intelligence

We are looking for a Senior Machine Learning Engineer to join our growing R&D team. You’re the perfect fit for this role if you are passionate about solving problems in NLP, have a great appreciation for science and want to transform how it is done. Reporting into the Engineering Manager, R&D.

Pay range: $160,000 - $210,000 CAD

We know compensation is an important part of choosing your next role. The range shown reflects our target hiring range, informed by market data, internal equity, and the role’s current scope. Often the mid-range is where we tend to fall, but individual offers may vary based on experience, skills, and the role scope.

• Build and deploy machine learning models over omics data (e.g. genomics, transcriptomics, proteomics, epigenomics, and multi-omics), capturing biological structure, variability, and experimental context.
• Work with major omics and biomedical databases (e.g. gene, protein, pathway, interaction, and expression resources) to integrate heterogeneous biological signals into unified learning pipelines.
• Develop and apply foundation models for biological data, including sequence-based, expression-based, and multi-modal models, adapting them to downstream scientific and product use cases.
• Design ML systems that populate, enrich, and reason over a biological knowledge graph, connecting entities such as genes, proteins, pathways, phenotypes, diseases, and experimental evidence.
• Apply graph-based methods tailored to biology, including graph embeddings, message passing, and network-aware learning, to model molecular interactions and biological systems.
• Collaborate with Bench Sci’s Science team to ensure models reflect biological constraints, experimental design, and domain nuance, not just statistical patterns.
• Power downstream experiences by surfacing insights through semantic search, recommendation, and conversational AI / chat-based scientific assistants.
• Improve scalability, robustness, and interpretability of models operating on large, sparse, noisy, and biased omics datasets.
• Lead technical decision-making within the ML team, mentor other engineers, and help define best practices for applied ML in biomedical settings.
• Own projects end-to-end, from data exploration and model prototyping to production deployment and monitoring.
• Continuously improve the performance and scalability of ML models that are at the core of Bench Sci’s products.
• Regularly investigate what technologies will best enable Bench Sci to effectively generate use cases.
• Advocate for code and process improvements across your team, and help to define best practices based on personal industry experience and research.
• Participate in sprint planning, estimation and reviews. Take ownership of deliverables, and work with teammates to ensure high-quality deliverables.

• Bachelor’s degree or higher in Computer Science, Mathematics, Machine Learning, Bioinformatics, or a related field.
• Leadership: 2+ years of tech lead experience in a production ML environment.
• Hands‑on experience working with omics data and omics derived resources, such as genomic sequences, expression matrices, protein data, or biological networks.
• Familiarity with omics and biomedical databases (e.g. gene/protein annotations, interaction networks, pathway databases, expression atlases).
• Experience with or strong interest in biological foundation models, such as sequence models, embedding models, or multi‑modal models applied to molecular or cellular data.
• Solid understanding of graph methods in a biological context, including knowledge graphs, molecular interaction networks, or pathway‑level representations.
• Experience applying NLP or LLM‑based techniques to scientific text or integrating text‑based evidence with structured biological data.
• Strong experience with Tensor Flow, PyTorch, and Omics processing libraries.
• Comfort working across disciplines, collaborating closely with scientists, engineers, and product teams.
• A team player who strives to see teammates succeed together.
• A growth mindset, strong ownership mentality, and desire to work on scientifically meaningful problems.
• You have a constant desire to grow and develop.

• Research…