Lead the design, implementation, and evaluation of agentic AI solutions that enhance eCommerce platforms, collaborating with product and engineering teams across the UK, Vietnam, and globally.
Locations (3)
Toronto, Canada
United Kingdom
Ireland
Compensation
UK: GBP 90,000 – 100,000 a year
Dublin, Ireland: EUR 105,000 – 115,000 a year
Responsibilities
Assess new agent capabilities, protocols, models and infrastructure and recommend adoption strategies
Review AI agent integration with the cloud intelligence layer, including discovery, merchant context, identity and permissions
Build working prototypes and reference code to validate concepts
Collaborate with product and engineering partners to transition experiments into production
Represent Clearer at industry events such as Shopify DotDev and build external relationships
Designs, develops and deploys the evaluation and trust capabilities of Equinix's GenAI platform, building features for agent evaluation, trust controls, and root‑cause analysis.
Compensation
The targeted pay range for this position in the following location is / locations are:
Canada - Toronto Office TRO : 102,000 - 152,000 CAD / Annual
Responsibilities
Build platform features for evaluation: dataset creation and versioning, golden datasets, synthetic test data and reusable metric templates
Build scoring for agents: code‑based checks, LLM‑as‑judge, tool‑call accuracy and grounding checks
Build human‑review features that compare judge scores against human reviewers, and recalibrate when agreement is low
Run evaluations at scale: before release, on every change to a prompt, model or tool, and on agents in production
Build evaluation playbooks so agent builders can evaluate their agents without specialist help
Requirements
Bachelor's degree in computer science, engineering, data science, mathematics or a related field
Nice to have: evaluation or observability features used by many teams or at scale
Nice to have: LLM evaluation and observability tools
Nice to have: OpenTelemetry
Nice to have: red‑teaming or adversarial testing of LLM systems
Experienced software engineer with experience with the Microsoft full stack (.net / Sql Server) who can create requirements, digest, plan, and work through complex problems, while also mentoring junior developers.
Compensation
Along with the benefits listed above, the career level salary range for this role is $84,000 to $165,000.
Responsibilities
Participate in SDLC estimation and development processes and providing light/general guidance to team members
Determine & define technical solutions of any items
Review other developers' design solutions
Create designs to reduce or eliminate maintenance and ongoing manual testing validation
Design, build, and ship products and services that shape how pharma commercial teams engage with healthcare providers, working across the stack with product, design, and ML engineering.
Compensation
Final compensation is determined through a comprehensive review of the successful candidate’s unique skill set, specialized experience, certifications and other relevant considerations. As such, the final offer may vary based on these specific factors. Salary expectations will be discussed collaboratively early in the process to ensure alignment.
Responsibilities
Own significant product capabilities end-to-end, from ambiguous problem framing through design, implementation, deployment, and iteration with minimal oversight
Build accessible, data-rich user experiences with React and TypeScript
Build and maintain real-time computation services (AWS Lambda)
Design and maintain reliable, scalable APIs and backend services
Lead technical design, code review, and architectural discussions
Lead engineering teams behind back-end platforms powering RBC's Retail Trading business, managing developers and team leads, setting technical direction, and overseeing modernization and AI initiatives.
Compensation
A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable
Responsibilities
Manage developers and team leads across Java and HPE NonStop/COBOL
Set priorities and allocate resources to deliver with quality and efficiency
Build a high-performing, continuous-learning culture
Provide leadership and technical expertise to oversee design, development, and deployment of complex software solutions
Make independent decisions on complex technical and business issues
Requirements
Nice to have: Retail brokerage or trading domain knowledge
Ephla Bio is looking for a bioinformatician to turn biological and experimental data into insights that guide therapeutic development. Build reproducible analysis pipelines, interpret complex datasets, and apply statistical methods to target prioritization.
Locations (3)
Toronto, Ontario, Canada
San Francisco Bay Area, California, United States
New York, New York, United States
Responsibilities
Build and validate workflows from tumor/normal sequencing (WES/WGS, RNA-seq) through QC, somatic variant calling, VAF and clonality analysis (truncal vs. subclonal, adjusted for purity and copy number), HLA typing, expression quantification, and reporting.
Analyze HTS data and other large-scale screens. Integrate them with public datasets (e.g. immunopeptidomics) and turn patterns into testable hypotheses.
Assess assay quality, batch effects, and biological variability. Benchmark methods, validate findings against independent datasets or experimental results, and communicate uncertainty early.
Lead product security for Vecima’s software and hardware platforms, driving threat modelling, secure design, and vulnerability management across embedded Linux and cloud‑connected products.
Locations (2)
United States
Canada
Responsibilities
Identify and reduce security risk throughout the product lifecycle
Lead threat modelling for new products and architecture changes
Translate security risks into engineering requirements
Coach developers and architects in secure design and coding
Improve secure product development through risk‑based activities and automated CI/CD checks
Combine assay results, sequence annotations, and relevant biological evidence to rank candidates and explain the rationale for experimental follow-up.
Design experiments with lab scientists (scale, controls, data priorities), turn predictions into testable candidates, and feed results into the next round.
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