Machine Learning Operations (MLOPS) Engineer
Senior Data Scientist / MLOps Engineer
Location: Montreal
Employment Type: Full-time
Location Type: Hybrid co-working space shared at the MILA (Mila – Quebec AI Institute) office in Little Italy 6666 Rue Saint-Urbain, Montreal QC H2S 3H1
Department: Engineering / Data & ML
Who We Are
At Soma Energy, we’re transforming the power grid with cutting-edge AI, making energy systems smarter, cleaner, and more resilient. Our energy intelligence platform integrates renewables, batteries, and large-scale energy loads, unlocking unprecedented flexibility and sustainability.
Backed by leading Bay Area venture capital firms, Soma is an early-stage startup founded by energy domain experts with over 20 years of experience in electricity markets and renewable energy procurement. Our founding team created the energy optimization program at AWS and includes leading PhDs in AI and optimization.
We’re building a diverse, ambitious team driven by curiosity, creativity, and a passion for solving real-world climate and energy challenges with modern AI. If you’re excited by the intersection of AI, data, and energy systems, we’d love to meet you.
About the Role
We’re looking for an exceptional Senior Data Scientist / MLOps Engineer to help design, build, and scale the machine-learning systems that power Soma’s Energy Intelligence Platform.
This role sits at the intersection of data engineering, applied machine learning, and production systems. You’ll work closely with software engineers, optimization researchers, and energy experts to take ML models from experimentation to reliable, high-performance production systems used in real-time grid operations.
This role is ideal for someone who has worked at an AI-first company, thrives in fast-paced environments, and enjoys owning the full ML lifecycle—from data pipelines to deployed models.
Key Responsibilities
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Design, build, and maintain end-to-end ML pipelines, from data ingestion and feature engineering to model training, evaluation, and deployment.
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Productionize machine-learning models into scalable, reliable systems used in real-time energy optimization.
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Develop and operate MLOps infrastructure for model versioning, monitoring, retraining, and performance tracking.
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Collaborate with backend engineers to integrate ML services into distributed, high-availability systems.
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Optimize time-series and geospatial data workflows for large-scale, mission-critical applications.
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Lead technical design discussions related to ML systems, data architecture, and deployment strategies.
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Continuously improve model performance, system reliability, and deployment velocity in a production environment.
What We Look For
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BS or MS in Computer Science, Engineering, Data Science, or a related field.
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7+ years of experience in data science, machine learning engineering, or data engineering roles.
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Proven experience working at an AI-first or ML-driven company.
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Strong hands-on experience with production ML systems (not just experimentation).
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Proficiency in Python and experience with ML frameworks and data processing libraries.
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Experience with MLOps practices: model deployment, monitoring, CI/CD for ML, reproducibility.
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Experience with distributed systems and data pipelines (e.g., Spark, Airflow, streaming systems).
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Familiarity with cloud platforms (AWS or GCP) and infrastructure-as-code tools (Terraform, CDK).
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Strong understanding of performance, reliability, and scalability tradeoffs in ML systems.
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Ability to work with high autonomy in a fast-paced startup environment.
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Excellent written and verbal communication skills.
Nice to Have
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Experience with optimization, forecasting, or time-series modeling.
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Exposure to energy systems, infrastructure, or climate tech.
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Experience working with real-time or low-latency ML systems.
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Background collaborating closely with research or applied science teams.
Compensation & Benefits
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Compensation: $178,000 – $256,000 + equity
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Comprehensive health and wellness benefits
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Opportunity to work on mission-critical AI systems with real-world climate impact
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High ownership, high agency, and the ability to shape core ML infrastructure from the ground up
Our Commitment
We thrive on diverse perspectives, skills, and experiences to push the boundaries of AI-driven energy solutions. We hire based on talent and potential and are proud to be an equal-opportunity employer. If you need accommodations during the hiring process, let us know—we’re happy to support you.
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