Senior Machine Learning Engineer, Loopio
Library
CA$141.5-188.7k
+ Equity
RFP software provider
Be an early applicant
RFP software provider
201-500 employees
Be an early applicant
CA$141.5-188.7k
+ Equity
201-500 employees
Company mission
To empower clients to shine in every response.
Role
Who you are
- We are seeking a highly skilled Senior Machine Learning Engineer with deep expertise in NLP, large language models, and model deployment
- 4+ years applying ML in production with practical depth in NLP, LLM workflows, embeddings, or retrieval augmented systems
- Hands-on and deep experience with transformer models, embedding-based methods, and retrieval augmented techniques, prompt structured or fine-tuned LLMs, for reasoning and generation tasks
- Ability to turn prototypes into stable engineering solutions that perform consistently in production environments
- Strong proficiency in Python, modern ML frameworks such as PyTorch or TensorFlow, and API or microservice development
- Experience building scalable and reliable ML services with attention to latency, observability, testing, deployment patterns, and runtime durability
- Ability to design robust agentic components, including control flow, state management, and integrations with retrieval and knowledge systems
- Ability to frame technical decisions in terms of customer impact, product value, and measurable improvements to workflow efficiency or answer quality
- Strong collaboration skills with cross-functional partners to align priorities and drive clear, grounded execution
- Comfort operating with ownership and urgency in a fast-moving environment focused on delivering meaningful outcomes
What the job involves
- This role focuses on building and operationalizing the core ML components that power our next generation search, summarization, and answer generation capabilities
- You will work closely with Applied Scientists, Product, and Engineering to bring advanced models from prototype to reliable, scalable production systems
- Build and productionize LLM and NLP models across retrieval, summarization, classification, and generative tasks by developing optimized embedding pipelines, prompt strategies, and fine-tuning methods while translating experimental prototypes into robust components that consistently perform under production conditions
- Improve model accuracy, relevance, and robustness through structured evaluation frameworks, systematic error analysis, and iterative experimentation that ensures predictable behaviour over time
- Design and implement scalable ML services and inference pipelines in Python using modern ML frameworks, incorporating efficient serving strategies such as batching, caching, streaming, and performance-tuned deployment patterns that meet latency, reliability, and throughput requirements
- Integrate models with retrieval systems, feature stores, and knowledge sources while applying strong engineering discipline in automated testing, observability, logging, and operational readiness to support durable, maintainable ML systems
- Contribute to core architectural decisions that balance modeling complexity with performance, maintainability, and long term extensibility
- Translate complex NLP and LLM product requirements into structured engineering plans with clear milestones while collaborating closely with Product, Engineering, and Applied Science partners to align expectations, remove constraints, and deliver measurable customer impact
- Participate in technical design reviews and champion improvements in ML engineering practices including deployment standards, model QA, code quality, and operational excellence while providing informal mentorship on modern NLP techniques and scalable model serving
- Demonstrate strong ownership and attention to detail by driving high-quality delivery in a fast-moving environment focused on reliability, clarity, and meaningful outcomes
Our take
Loopio provides RFP response software to companies in a variety of industries including healthcare, financial services and telecommunications. Its platform streamlines responses to proposal requests, requests for information and due diligence questionnaires, helping firms to improve sales efficiency and unlock growth opportunities.
The company boasts an impressive client list including DocuSign, PayPal and American Express. Loopio expects its client base to continue growing, bolstered by a 2023 independent study that shows that clients of the company can experience a three-year return on investment alongside almost $6 million in business benefits.
Loopio has used funding to accelerate product development and customer acquisition, as well as expand its team. It has also partnered with Door, a digital due diligence platform for asset management and research teams. This partnership exemplifies a mature caution in Loopio's expansion aims, helping them to keep in control of their growing business.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
9% employee growth in 12 months
Company
Funding (2 rounds)
Apr 2021
$200m
GROWTH EQUITY VC
Feb 2018
$8.7m
SERIES A
Total funding: $208.7m
Company benefits
- Robust Health Benefits
- Extended Parental Leave
- Employee Stock Options
- Professional Mastery Budget
- Remote Work Subsidies
- Annual Company Retreat
- Generous Paid Time Off Program
- Team Socials
Company HQ
Downtown Yonge East, Toronto, ON
Leadership
Zak Hemraj
(Co-Founder & CEO)Prior to co-founding Loopio, Zak spent 8 years at Achievers, joining as a Software Developer before moving into the role of Sales Engineer an then Manager and Team Lead of Software Development.
Matt York
(Co-Founder & CTO)Matt previously worked as a VP of Engineering at Top Hat and as a Software Developer & Manager at Achievers.
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