Machine Learning Engineer, Safety and Customer Care AI

United States Digital Space LLC; Toronto; On-site; CAD 119,000 - 149,000; Full time

Benefits offered by this job

Extended health and dental coverage

Mental health benefits

Family building benefits

Child care and pet benefits

Health Care Savings Account

RRSP plan with company match

Flexible paid time off

Paid parental leave

Job summary

United States Digital Space LLC is hiring a Machine Learning Engineer for the Safety and Customer Care team in Toronto. You will fine-tune open-source LLMs, build AI agents, and create end-to-end safety workflows across post-training, evaluation, and production deployment.

You will work with LangGraph or equivalent, iterate on data signals, and collaborate with product and data science to improve real-time support at scale.

Qualifications

• 3+ years of industry experience in applied ML/AI, including an MS or PhD in CS/ML.
• Hands-on with open-source models and fine-tuning methods (SFT/LoRA/RLHF/RLAIF/RLVR).
• Experience building AI agents and shipping them to production.
• Experience with ML/AI evaluation, offline/online metrics.
• Production deployment of ML/AI in real-time systems.
• Strong Python and PyTorch skills.

Responsibilities

• Fine-tune and align models and build AI Agents for safety-critical support.
• Design tools and control flow in LangGraph for agent lifecycle.
• Develop, evaluate, and productionize AI agents and evaluation frameworks.
• Ship models and agents with monitoring and guardrails for millions of interactions.

Skills

Python

PyTorch

SFT

LoRA

RLHF

RLAIF

RLVR

LangGraph

Agent development

LLM evaluation

Education

MS in Computer Science

PhD in Computer Science

Tools

LangGraph

Job description

At the company, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

The Safety and Customer Care (SCC) team at the company manages over 1.7 million monthly human and AI interactions and serves as the company's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with the company, transforming every support interaction into a moment of genuine connection.

Agentic AI is at the center of how we scale that mission. We fine-tune and align open-source models, build AI-powered support agents, and develop end-to-end AI agents for safety case management, systems that reason over complex, high-stakes cases and drive them to resolution. SCC brings together ML, data, backend, and product engineers alongside data scientists and operations partners to transform these systems.

As a Machine Learning Engineer on the SCC team, you will fine-tune and align models and build AI Agents that power how riders and drivers get help. Your work spans the full loop: post-training open-source models for our domain, composing them into multi-step agents, and building the evaluation that proves they are safe to ship in a customer-facing, safety-critical setting.

• Post-train and adapt open-source LLMs for SCC use cases using SFT, LoRA, and preference-tuning methods (RLHF, RLAIF, RLVR).
• Design and build AI-powered support agents and end-to-end agents for safety case management using LangGraph or equivalent agentic frameworks.
• Own the evaluation data flywheel, offline and online, that defines what "good" looks like and build benchmarks for the team to hill-climb.
• Turn interaction feedback into training data and learning signals, closing the data flywheel that continuously improves the models.

Responsibilities:

• Conduct literature review and build post-training framework and lifecycle. Curate and process human and synthetic data for SFT/LoRA/RLHF/RLAIF/RLVR, and iterate on model quality for real support and safety tasks.
• Develop, evaluate, and productionize AI agents, designing tools, state, and control flow in LangGraph (or equivalent) and taking them through the full agent development lifecycle.
• Build and scale evaluation frameworks, golden sets, rubric-based grading, LLM-as-judge where appropriate, and regression testing.
• Ship models and agents into real-time production, with the monitoring and guardrails needed to operate them safely at millions of interactions a month.
• Apply traditional ML (classification, ranking, gradient-boosted trees) where it's the right tool, and partner with product, ops, and data science to scope problems and define success metrics.

Experience:

• 3+ years of industry experience in applied ML/AI, inclusive of an MS or PhD in Computer Science, Machine Learning, Artificial Intelligence or a related technical field.
• Post-training experience with open-source models. Hands-on familiarity with fine-tuning and preference-tuning paradigms such as SFT, LoRA, RLHF, RLAIF, and RLVR.
• Agentic development experience. Built and shipped agents with LangGraph or equivalent frameworks, and comfort with the full agent development lifecycle.
• Experience with AI/LLM evaluation. Designed metrics and built offline/online evaluation for generative systems.
• Experience deploying ML/AI applications to real-time production use cases.
• Strong programming skills in Python and hands-on experience with PyTorch.
• Preferred:
  • Experience applying ML/AI to customer support or trust & safety workflows — agent assist, routing, resolution recommendation, or abuse/safety detection.
  • PhD in Computer Science, Machine Learning, Statistics, or a related technical field.
  • Publications at top-tier peer-reviewed research venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR).

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a the company funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under the company's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service
• the company is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and the company ride credits

the company is committed to creating an inclusive workforce that fosters belonging. the company believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. the company also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

the company highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. the company considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks the company offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $118,800 - CAD $148,500, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

the company may use artificial intelligence to screen applicants, however, the company employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.