ZipRecruiter

Log In

1

Temporary Meta Machine Learning Jobs in Toronto, ON

Passage

Senior Marketing Engineer

Toronto, ON · On-site

CA$100K - CA$150K/yr

... the machine doesn't run on paid alone. * Run everything through the platform -- and extend it ... Meta, Google, TikTok, LinkedIn, X) -- at API depth, not just the UI: rate limits, learning phases ...

Td

Software Engineer III (AI Integration)

Toronto, ON · On-site +1

CA$125K - CA$154K/yr

... temporary market premium specific to this role that is reassessed annually. TD is committed to ... This role combines software engineering, data science, and machine learning expertise to develop ...

SPS North America

Office Services Specialist

Toronto, ON

CA$27 - CA$29/hr

Ensure refrigerators, microwaves, coffee machines, dishwashers, and other pantry equipment are ... Learning & Development: At SPS, we promote a work culture of learning so that you can develop to be ...

previous page

Showing results 21-34

All JobsMeta Machine Learning JobsTemporary Meta Machine Learning JobsTemporary Meta Machine Learning Jobs in Toronto, ON

Temporary Meta Machine Learning information

What is a temporary Meta machine learning job?

Temporary Meta Machine Learning jobs are short-term positions at Meta (formerly Facebook) that focus on developing, deploying, or researching machine learning models and technologies. These roles may support ongoing projects, fill gaps during employee leave, or address spikes in workload. Responsibilities can include data preprocessing, model training, evaluation, and collaborating with cross-functional teams. Temporary roles often give candidates exposure to Meta's cutting-edge AI tools and processes, and may sometimes lead to permanent opportunities.

What are the key skills and qualifications needed to thrive as a temporary Meta machine learning engineer?

To thrive as a Temporary Meta Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning, typically with experience in Python and relevant ML frameworks. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms, and version control systems is often required, along with a proven ability to rapidly learn new technologies. Strong problem-solving skills, adaptability, and effective communication are essential for collaborating within dynamic teams and meeting project goals on tight timelines. These skills ensure that you can quickly contribute to impactful ML projects, deliver results efficiently, and integrate well into fast-paced, innovative environments.

What are some common challenges faced by professionals in temporary machine learning roles at Meta, and how can they be addressed?

Professionals in temporary machine learning roles at Meta often encounter challenges such as quickly acclimating to complex codebases, integrating with established teams, and delivering impactful results within a limited timeframe. Success in these roles typically requires strong technical skills, adaptability, and effective communication. Proactively seeking guidance, leveraging available documentation, and collaborating closely with permanent team members can help overcome these hurdles and maximize contributions during the temporary assignment.

What is the difference between Temporary Meta Machine Learning vs Data Scientist?

AspectTemporary Meta Machine LearningData Scientist
CredentialsTypically requires a background in computer science, statistics, or related fields; certifications in machine learning or data analysis are commonRequires a degree in computer science, statistics, or related fields; certifications like Certified Data Scientist are advantageous
Work EnvironmentProject-based, often contract roles within tech companies, startups, or consulting firmsFull-time or contract roles in various industries including finance, healthcare, and tech
Industry UsagePrimarily in tech, AI, and machine learning-focused companiesWidely used across multiple industries including finance, healthcare, marketing, and tech

Temporary Meta Machine Learning roles focus on short-term projects involving machine learning model development and deployment, often requiring specialized technical skills. Data Scientist roles are broader, encompassing data analysis, statistical modeling, and insights generation across diverse industries. While both roles require strong analytical skills and technical knowledge, Temporary Meta Machine Learning positions are more specialized in AI and machine learning applications.

What are popular job titles related to Temporary Meta Machine Learning jobs in Toronto, ON?

For Temporary Meta Machine Learning jobs in Toronto, ON, the most frequently searched job titles are:

What job categories do people searching Temporary Meta Machine Learning jobs in Toronto, ON look for?

The top searched job categories for Temporary Meta Machine Learning jobs in Toronto, ON are:

Temporary Meta Machine Learning jobs near you

Senior Marketing Engineer

Passage

Toronto, ON • On-site

CA$100K - CA$150K/yr

Full-time

Posted 10 days ago

Job description

About Passage

Passage helps international students navigate their journey abroad — from choosing a program and getting admitted to financing their studies and helping with their visa.

The role

You'll own Passage's marketing channels end to end — paid and organic, performance and brand. Meta and Google Ads are live across 15 countries today, organic search is instrumented, and TikTok, LinkedIn, and X are next. You'll decide where the money and effort go, keep the brand sharp on every surface, and answer for the numbers.

What makes this a marketing engineer role: at Passage, marketing runs on an AI-assisted platform built in-house — campaigns are launched, creative is built, audiences are sized, and performance is read in conversation with Claude, on top of real engineering (ads-API launch engines, MCP servers, policy guardrails). You'll operate through that platform and keep extending it. We're looking for the rare person who can both make the call to scale a channel and build the automation that scales it.

What you'll do

  • Own the paid channels. Run Meta and Google Ads across 15 countries — budgets, audiences, creative rotation, scale/kill decisions — and take TikTok, LinkedIn, and X from zero to measured, live channels.

  • Own marketing performance. A single cost-per-outcome metric ranks every dollar of spend across platforms. You'll run the weekly performance cadence, own funnel metrics from impression to conversion, and make budget moves with numbers behind them.

  • Own the brand across every surface. Our brand system (palette, themes, type, tone, per-medium rules) is encoded so AI produces on-brand work by default. You'll hold that bar as channels and languages multiply — ad creative, carousels, programmatic video, decks, and social posts, localized across 15+ languages.

  • Grow organic alongside paid. Search Console and SEO, an organic social presence built on the same brand system, and content that feeds the funnel — so the machine doesn't run on paid alone.

  • Run everything through the platform — and extend it. Launch engines against the ads APIs (dry-run by default, created-paused, idempotent, policy-enforcing), in-house MCP servers, and Claude skills for the recurring workflows. When something is manual and repetitive, you automate it; when a new channel arrives, you wire it in.

  • Measure honestly. UTM and naming conventions designed as data (they feed our BigQuery warehouse and attribution models), pixels and conversion events per platform, and dated cross-platform analyses that become the team's institutional memory.

Channels & stack

Channels: Meta · Google Ads · TikTok (next) · LinkedIn · X · organic search & social Stack: Python (stdlib-first) · Meta Marketing API · Google Ads API / GAQL · Google Search Console · Claude Code + MCP (agent tooling) · Remotion (programmatic video) · BigQuery · policies-as-code by PR

You might be a great fit if you

  • Have 4+ years spanning performance marketing and engineering — you've run real budgets and written production code (strong Python preferred).

  • Have gone deep on at least two major ad platforms (Meta, Google, TikTok, LinkedIn, X) — at API depth, not just the UI: rate limits, learning phases, targeting quirks, attribution windows.

  • Have genuine brand sensibility — you notice type, palette, and tone, and can tell on-brand from almost-on-brand at a glance.

  • Are data-fluent: SQL, funnel math, attribution; comfortable defining a metric and defending it to leadership.

  • Have safety instincts — dry runs, guardrails, idempotency — because your code and your decisions touch live ad spend.

  • Are AI-forward: excited to work through agents and build the tooling that makes them safe and effective.


Nice to have

  • International, multilingual campaigns; localization at scale.

  • SEO or content-marketing background.

  • Programmatic creative or video (Remotion or similar).

  • Experience building MCP servers, LLM agents, or AI workflow tooling.

  • BigQuery/dbt or analytics-engineering exposure.

  • Early-startup experience owning a function solo.

What success looks like in the first six months

  • Every live channel runs through you — budgets, creative, and the weekly performance review — and at least one new channel (TikTok, LinkedIn, or X) is launched and honestly measured.

  • The brand is consistent everywhere, even as languages and surfaces multiply.

  • Leadership sees per-channel cost-per-outcome they trust, and the platform has grown wherever the work was manual.
    Ready to join us?

    We are building a company designed to expand access to life-changing opportunities around the world.
    If you are excited about building systems, solving complex problems, and shaping how a company makes decisions, we would love to meet you.
    Note: Artificial intelligence may be used in the screening or assessment of applicants.


    If your experience does not match every requirement but you believe you can thrive in this role, we encourage you to apply.

    Compensation:
    C$100K to C$150K + Offers Equity