Senior Machine Learning Engineer
28 May 2024
Toronto, ON, Canada
Verified by Turrior
Content + Source + Freshness • 18 Dec 2025 • 95% confidence
85 / 100
Offer value
High value due to the advanced technical skills required, potential for significant impact in the field of ML, and competitive growth opportunities.
- Engage in cutting-edge ML projects with significant industry impact.
- Competitive compensation and career advancement opportunities.
- Work remotely with a focus on continuous learning.
Pros
- Strong market relevance with an emphasis on ML in healthcare.
- Opportunity to lead innovative projects in NLP and computer vision.
- A supportive remote-first culture that fosters personal growth.
Cons
- Requires a high level of technical expertise (5+ years experience).
- Intensive workload could lead to challenges in work-life balance.
- Possibility for high-pressure scenarios during project deadlines.
Who it's for
Senior • Remote
Good fit
- Senior ML engineers eager to make an impact in healthcare.
- Candidates proficient in AI technologies seeking growth.
- Professionals comfortable with high-stakes, fast-paced roles.
Not recommended for
- Those new to machine learning or related fields.
- Candidates preferring roles with less technical responsibilities.
- Individuals resistant to rapid technological advancements.
Motivation fit
Desire to work in pioneering ML applications.Interest in data-driven decision making within the life sciences.Willingness to adapt and grow in a fast-paced environment.
Key skills
Machine learning and NLP expertise.Proficiency with TensorFlow and PyTorch.Ability to lead technical discussions and projects.
Score: 85/100 AI verified analysis
About the job
We are looking for a Senior Machine Learning Engineer to join our growing engineering 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, Engineering.
You Will:
- Continuously improve the performance and scalability of ML models that are at the core of BenchSci’s products
- Leading and providing technical direction for BenchSci’s ML team
- Work with BenchSci's Product Managers and Scientists to correctly capture the nuances of biology
- Lead or consult the authoring of engineering design proposals following the unified Platform Stream roadmap at BenchSci
- Leverage a deep understanding of the business context and the team’s goals to unlock independent technical decisions in the face of open-ended requirements
- Proactively identify new opportunities (from both internal and external sources) and advocate for and implement improvements to the current state of projects
- Respond with urgency and drive urgency in own team to operational issues, owning resolution within one's sphere of responsibility
- 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
You Have:
- 5+ years of software development experience
- Bachelor’s degree in Computer Science or Mathematics
- Strong experience with NLP
- Experience with computer vision problems
- A degree in computer science with a focus in machine learning and at least three years of experience in the industry
- Strong experience with TensorFlow, PyTorch, and image processing libraries such as OpenCV and scikit-image
- Experience with data processing frameworks
- You have a constant desire to grow and develop
- You have strong cross-team communication and collaboration skills
- A team player who strives to see teammates succeed together
Nice to have:
- Experience with GCP and its tools including VertexAI and BigQuery
- Experience implementing proprietary LLMs/Multimodal LLMs like Gemini or GPT series
- Experience implementing and/or fine-tuning open-sourced LLMs/Multimodal LLMs
- Research publications in ML/AI-related fields
- Background in Life Science (cell biology, molecular biology, genetics, immunology, microbiology)
