Sennos

About Sennos
Sennos is the pioneer and leader in automated fermentation monitoring and analysis. We unify cutting-edge hardware, AI-powered analytics, and intuitive interfaces to deliver the industry's most advanced sensing platform. Our mission is to transform the Fluid, Fermentation, and Bio-manufacturing industries by replacing outdated systems and practices with intelligent sensing and AI-driven predictive control. We are building the future today so our solution can anticipate, adapt, and evolve to unlock a new era of industrial creativity, sustainability, precision, and resilience.
Position Summary
The Senior ML / AI Engineer sits within the Data Team's AI, ML, and Data Science pod, supporting R&D, product development, and the build-out of Sennos' advanced machine learning capabilities. This is a new role on a new team, evolving from existing data engineering and data science capabilities into a dedicated machine learning engineering function. The role serves as a technical authority on advanced machine learning - helping Sennos formulate complex modeling problems, design rigorous experiments, build and evaluate sophisticated models, and move successful approaches into reliable production. This role operates in a high-ambiguity, early-stage environment. The ideal candidate is deeply hands-on and technically authoritative, comfortable moving fluidly between research-oriented exploration and production-oriented implementation, with sound judgment about when to experiment, when to simplify, and when a model is ready to operationalize.
Responsibilities

• Lead the design and development of advanced machine learning models for complex scientific, product, and business problems, including problems that require unsupervised or other non-standard modeling approaches
• Translate ambiguous real-world problems into well-structured machine learning experiments - defining objectives, selecting appropriate modeling approaches, designing evaluation strategies, and determining whether results are meaningful and actionable
• Build, compare, and evaluate machine learning models with rigorous attention to statistical validity, model behavior, generalization, interpretability, and practical usefulness
• Productionalize successful models and ML workflows, moving work from experimentation into reliable, maintainable systems that can be monitored, retrained, evaluated, and supported over time
• Implement practical MLOps patterns appropriate to the models being developed, including reproducibility, model versioning, monitoring, drift detection, retraining workflows, evaluation frameworks, rollback planning, and lifecycle management
• Help define Sennos' advanced ML practices, including experimentation standards, model development patterns, evaluation methods, documentation expectations, and approaches to model governance
• Identify gaps in the tooling, infrastructure, and development environment required to support advanced ML work, and help define the capabilities needed to close those gaps in partnership with appropriate technical teams
• Partner closely with Data Engineering, Analytics, Science, Product, and other stakeholders to identify high-value ML opportunities, clarify problem definitions, and ensure model outputs are useful in real workflows and products

Required Qualifications
Education:

• Bachelor's degree in Computer Science, Statistics, Mathematics, or related quantitative field; advanced degree preferred; equivalent experience considered

Experience:

• Significant hands-on experience designing, developing, evaluating, and productionalizing machine learning models in real-world environments
• Deep understanding of machine learning and applied statistics, including the ability to formulate complex modeling problems and select appropriate methods based on the underlying data, objectives, and constraints
• Demonstrated experience designing and executing rigorous ML experiments, including supervised and unsupervised approaches, model comparison, validation, and evaluation where ground truth may be incomplete or imperfect
• Strong Python skills and practical experience writing maintainable code for machine learning, statistical modeling, experimentation, and production workflows
• Demonstrated experience moving models from experimentation into production, including deployment, monitoring, retraining, reproducibility, versioning, testing, and lifecycle management
• Strong judgment about when advanced machine learning is warranted and when simpler statistical, deterministic, or rules-based approaches are more appropriate
• Ability to independently investigate ambiguous modeling problems, develop a technically sound approach, and communicate the reasoning, limitations, and trade-offs behind that approach
• Experience helping establish or improve machine learning practices, technical standards, experimentation patterns, or model development processes within a team or organization
• Ability to work effectively with product, science, data, and engineering stakeholders while remaining the technical authority on the machine learning approach
• Strong organizational and communication skills, with the ability to operate independently, create structure in an emerging function, and guide others on advanced ML methods and practices

Skills:

• Advanced machine learning and applied statistics (supervised, unsupervised, probabilistic methods)
• Python and production-grade ML engineering (testing, versioning, reproducibility, monitoring)
• MLOps practices: model deployment, drift detection, retraining workflows, lifecycle management
• Experimental design and rigorous model evaluation
• Independent problem framing and technical communication with cross-functional stakeholders

Preferred Qualifications

• Experience with modern ML tooling such as feature stores, model registries, experiment tracking, orchestration, or model monitoring platforms
• Experience with Snowflake, Snowpark ML, Snowflake Feature Store, Snowflake Model Registry, AWS, or similar cloud and data-platform-based ML capabilities
• Experience building ML systems for scientific, industrial, sensor, IoT, manufacturing, fermentation, biotechnology, or other complex physical-world data domains
• Familiarity with generative AI and related techniques such as LLM workflows, retrieval-augmented generation, or structured prompting, alongside a strong foundation in traditional ML

Physical Requirements and Work Environment
This is primarily a desk-based role with standard office physical requirements.

• Ability to work at a computer for extended periods
• Minimal travel expected

Our company is currently active in 17 states (AZ, GA, ID, IL, FL, MA, ME, MI, MO, NC, NY, PA, TN, TX, OR, VA, and WA), and we prefer candidates located in one of these states for remote positions.
This job description is intended to convey information essential to understanding the scope of the position and is not an exhaustive list of skills, efforts, duties, responsibilities, or working conditions associated with it. Responsibilities may change according to business needs.
Please Note
Applicants must be permanently authorized to work for ANY employer in the United States. We are unable to sponsor or take over sponsorship of an employment visa at this time.
Recruitment Agency Notice:
We do not accept unsolicited candidate submissions. We only work with recruitment agencies that have a signed agreement with our HR team. Unsolicited resumes will not incur any fee obligation.
Equal Opportunity Statement
Sennos is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.