Staff Engineer, Machine Learning Life Sciences (AI)

Location: Cambridge, Massachusetts, USA; flexible hybrid work with time split weekly between the office and remote work

Salary: $148,530–$204,250 per year, plus short-term incentive, long-term equity, and a one-time new-hire stock option grant

Company

hirify.global is an agricultural biotechnology company developing predictive design and multiplex gene-editing technologies to improve seeds, crop productivity, and sustainability.

What you will do

• Build, deploy, maintain, and monitor production machine learning pipelines and infrastructure serving predictions at scale.
• Implement model versioning, monitoring, lifecycle management, and integrations with genomic, phenotypic, and biological data platforms.
• Use AWS and containerization technologies to integrate ML systems with research platforms.
• Partner with computational and experimental biologists to contextualize heterogeneous biological data and support research-critical modeling programs.
• Train and validate statistical and machine learning models, prototype new approaches, and assess production feasibility.
• Integrate third-party tools, foundation models, and AI agents while contributing to technical decisions, code reviews, and engineering standards.

Requirements

• MS or PhD in computer science, engineering, statistics, mathematics, computational biology, or a related field, or a BS with equivalent experience.
• 6+ years of machine learning engineering experience with a strong emphasis on production systems.
• Proven ability to deploy, maintain, and monitor ML models and pipelines at scale.
• Advanced scientific Python, including NumPy, Pandas, and scikit-learn, plus hands-on PyTorch and/or TensorFlow experience for training and deploying neural networks.
• Experience with AWS services including EC2, S3, and SageMaker; Docker; MLflow; and Airflow or an equivalent workflow orchestrator.
• Ability to collaborate with biologists and life scientists, translate between biological and ML perspectives, and communicate technical results across disciplines.

Nice to have

• Familiarity with genomic, transcriptomic, and proteomic data, biological file formats such as FASTA, GFF, VCF, and BAM, and DNA/RNA/protein sequence modeling.
• Knowledge of deep learning for biological sequences, including genomic transformers and protein language models.
• Experience with graph neural networks or network analysis tools such as networkx for modeling biological relationships.

Culture & Benefits

• Flexible hybrid work model with regular office presence in Cambridge.
• Medical coverage through PPO and HDHP plans with a company-funded HSA, plus dental and vision insurance.
• Flexible spending accounts, voluntary benefits, and a wellness program.
• 401(k) plan with company matching and flexible paid time off.
• Inclusive, cross-functional environment spanning computational biology, software engineering, crop science, and agriculture.