Staff ML Platform Engineer (MLOps)
for each heading only if content exists. Use - for any list of duties/requirements/benefits, and
for prose. Do not repeat job title as heading. Do not include Job ID etc. Remove boilerplate. Include only sections with real specific content. Must preserve all substantive info.
We need to parse the posting.
First paragraph: "Come join our Data team! High velocity, high trust, and high impact with a will to win. If that resonates deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world. At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale. Ready to make an impact? Apply today. Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit."
That's about the role/company. Could be "About the role" or "About the company". We'll use "About the role". It's prose.
Then "The OpportunityWe're seeking a Staff ML Platform Engineer (MLOps) to build the platform our ML and LLM-powered products run on." This is basically role description. Could be part of responsibilities.
Then sections: "What You'll Own" with bullet list of items: Assessment and plan, Platform foundations and optimization, LLM infrastructure and smart routing, Experimentation and safe rollout, Observability and traceability, Hands-on operations, Data and feature infrastructure, Standards, not sole ownership. Those are responsibilities.
Then "Required Experience" bullet list.
Then "Bonus Points" bullet list.
Then "Our Tech Stack for Data" bullet list.
Then "Your Education" prose.
Then "LocationRemote (CA/US). Toronto-based candidates are welcome to work from our office at 325 Front St West if they prefer, but it's optional, not a hybrid requirement." That's location info; could be part of "About the role" or separate? Not a required heading; but we could include as part of About the role or maybe "Location" not a listed heading; we only have set headings. Could embed location in About the role paragraph.
Then "Travel ExpectationsApproximately 2-3 trips per year, including our company off-site in August." That's schedule? Travel expectations could be part of Schedule? Not exactly schedule but travel. Could be part of Schedule section if we have content. The instruction: include a section ONLY when posting has real, specific content for it. Travel expectations is specific; but we don't have a "Travel" heading in allowed list. Could put under Schedule? Not exactly. Maybe we can treat as part of "Schedule" (though schedule usually work hours). But we could omit if not fitting. However we need to preserve all substantive info; we could embed travel expectations in About the role or Responsibilities? Probably better to add a "Schedule" section with travel expectations? But schedule usually refers to work schedule (full-time, part-time, remote). Not present. The posting does not mention work hours. So we should not create Schedule section if no content. Travel expectations could be considered part of "About the role" or maybe "Responsibilities"? Not really. Could add a note in About the role.
Compensation: "CompensationPay Range: USD $172,000-$215,000 (United States) / CAD $172,000-$220,000 (Canada)". That's Pay section.
Hiring Journey etc is boilerplate? It's about process; could be considered boilerplate? It's not EEO etc but still process description. Might be considered boilerplate? The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Hiring journey is not exactly those but could be considered application instructions? It's describing interview process. Might be considered application instructions; better to drop. The instruction says remove application instructions. So we should drop the Hiring Journey section.
Thus we need sections: About the role (prose), Responsibilities (list), Requirements (list), Qualifications (maybe same as Required Experience?), Skills (maybe Bonus Points?), Benefits (none explicit), Pay (compensation), Schedule (none). We need to decide mapping.
We have Required Experience list (required). Bonus Points list (nice-to-have). Could map Required Experience to Requirements, Bonus Points to Skills. Qualifications maybe same as Requirements? Could combine. But we need to avoid thin sections. Perhaps we can have Requirements (required experience) and Skills (bonus points). Qualifications maybe not needed if we already have Requirements. But we need to include a section only when posting has real specific content. We have content for Requirements and Skills. Could also have Qualifications as same as Requirements but that would be duplicate. Better to have Requirements and Skills.
Responsibilities: from "What You'll Own" list.
About the role: combine initial paragraph plus maybe location and travel expectations.
Pay: compensation.
We must not invent or summarize away detail. Must preserve all substantive information. So we need to include all bullet points exactly as given (maybe rephrase? Must preserve info, not necessarily exact wording but cannot lose detail). We'll keep the substance.
We need to format as HTML:
About the role
then ...
. For lists: Responsibilities
- ...
...
. Similarly for Requirements, Skills, Pay.
Pay: we can put Pay
Pay Range: USD $172,000-$215,000 (United States) / CAD $172,000-$220,000 (Canada)
.
We need to ensure we don't include any markdown fences.
Let's extract bullet points.
Responsibilities (What You'll Own):
- Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for what needs to change.
- Platform foundations and optimization: Own compute provisioning and environment management, keep training and serving environments reproducible, keep frameworks and packages current across services and model images, and tune latency, throughput, and spend, all without destabilizing production.
- LLM infrastructure and smart routing: Build the layer our LLM features run on, including smart routing that sends each request to the cheapest model that can handle it well, plus the prompt and response evaluation needed to prove quality holds when we route down.
- Experimentation and safe rollout: Give us a real discipline for A/B testing models before they are fully ramped: shadow deploys, canaries, holdouts, and success criteria agreed in advance, so a model earns its way into production instead of being switched on.
- Observability and traceability: Know within minutes when a recommender goes down or starts drifting, and be able to explain why: model and data monitoring, alerting, regression detection, lineage, and enough traceability to reproduce a questionable recommendation on demand or trace a prediction back to the inputs that produced it (we currently use Braintrust; comparable tooling counts too).
- Hands-on operations: Stay close enough to the running systems to operate them. You will work with the team to keep models online, but when something breaks in production you can dive in and help fix it, including models other people built.
- Data and feature infrastructure: Own how features are computed, stored, and served consistently between training and inference, and take on the data engineering the team needs along the way.
- Standards, not sole ownership: Establish the deployment and monitoring standards the rest of the team can run with. You are building shared ownership, not becoming the only person who keeps things online.
Required Experience (list):
- Staff-level, hands-on experience in MLOps, ML platform, or ML infrastructure (we're also open to Data Platform Engineer, ML Infrastructure Engineer, or Data Scientist backgrounds with strong platform ownership: the title on your last resume matters less than what you actually built)
- Experience standing up MLOps practice end to end: CI/CD for models, experiment tracking, model registries, deployment workflows, and monitoring
- Production experience with LLM-based systems: serving, prompt and response evaluation, routing across models and providers, and managing cost and latency tradeoffs. If you've done this with traditional ML systems and can show you pick up LLM tooling fast, that counts too
- Experience operating models in both batch and real-time serving contexts
- Hands-on with compute provisioning and environment management: containers, reproducible training and serving environments, and keeping frameworks and packages current on a fast-moving stack without breaking production
- Experience running controlled model experiments in production: A/B tests, shadow or canary deploys, holdouts, and the judgment to set success criteria before ramping
- Depth in observability and traceability for production ML: drift and regression detection, alerting that catches a recommender going down or going off the rails, lineage, and the ability to trace a prediction back to the inputs that produced it and reproduce it after the fact
- Hands-on operational experience: you have carried the pager or its equivalent, debugged production ML incidents under time pressure, and fixed systems you did not originally build
- Comfort doing the data engineering the platform needs: pipelines, feature computation and storage, and keeping training and serving features consistent
- A track record of walking into complex, fast-grown systems, diagnosing the real problems, and materially improving them
- Strong systems design ability: you can translate product needs into durable architecture and stay close enough to the code to build it yourself
Bonus Points:
- Interest in growing into model development yourself. This role starts on the platform side, but the line between platform and modeling is thin here, and we would rather hire someone who wants to cross it
- Feature store experience. We are early here, so you would be shaping it rather than inheriting it
- Experience evaluating AI/ML observability or LLM evaluation vendors, with judgment on when to buy versus build
- Background in mission-driven, workforce, or government-adjacent data environments
- Comfort mentoring a small data and engineering team while you build
Tech Stack for Data (maybe could be under Skills? But we already have Skills as bonus points. Could add a separate Skills section for tech stack? The instruction: Use - for any list of duties/requirements/benefits. Tech stack is a list; could be considered Skills. But we already have Skills for bonus points. Could combine? Might be better to have a separate "Skills" section for tech stack, and Bonus Points as "Preferred Qualifications"? But we only have allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We could map Bonus Points to Qualifications, and Tech Stack to Skills. Or Requirements to Required Experience, Qualifications to Bonus Points, Skills to Tech Stack. Let's decide.
We need to preserve all substantive info. So we need to include tech stack somewhere. Could be under Skills. Bonus points could be under Qualifications (preferred qualifications). That seems logical: Qualifications often include preferred qualifications. So:
- Requirements: Required Experience (mandatory)
- Qualifications: Bonus Points (preferred)
- Skills: Tech Stack
Thus we have sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Pay.
Check if any Benefits: none explicit. So omit Benefits.
Schedule: none explicit (travel expectations maybe but not schedule). We'll omit Schedule.
Now we need to embed location and travel expectations in About the role paragraph.
Let's craft About the role paragraph
for prose. Do not repeat job title as heading. Do not include Job ID etc. Remove boilerplate. Include only sections with real specific content. Must preserve all substantive info. We need to parse the posting. First paragraph: "Come join our Data team! High velocity, high trust, and high impact with a will to win. If that resonates deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world. At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale. Ready to make an impact? Apply today. Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit." That's about the role/company. Could be "About the role" or "About the company". We'll use "About the role". It's prose. Then "The OpportunityWe're seeking a Staff ML Platform Engineer (MLOps) to build the platform our ML and LLM-powered products run on." This is basically role description. Could be part of responsibilities. Then sections: "What You'll Own" with bullet list of items: Assessment and plan, Platform foundations and optimization, LLM infrastructure and smart routing, Experimentation and safe rollout, Observability and traceability, Hands-on operations, Data and feature infrastructure, Standards, not sole ownership. Those are responsibilities. Then "Required Experience" bullet list. Then "Bonus Points" bullet list. Then "Our Tech Stack for Data" bullet list. Then "Your Education" prose. Then "LocationRemote (CA/US). Toronto-based candidates are welcome to work from our office at 325 Front St West if they prefer, but it's optional, not a hybrid requirement." That's location info; could be part of "About the role" or separate? Not a required heading; but we could include as part of About the role or maybe "Location" not a listed heading; we only have set headings. Could embed location in About the role paragraph. Then "Travel ExpectationsApproximately 2-3 trips per year, including our company off-site in August." That's schedule? Travel expectations could be part of Schedule? Not exactly schedule but travel. Could be part of Schedule section if we have content. The instruction: include a section ONLY when posting has real, specific content for it. Travel expectations is specific; but we don't have a "Travel" heading in allowed list. Could put under Schedule? Not exactly. Maybe we can treat as part of "Schedule" (though schedule usually work hours). But we could omit if not fitting. However we need to preserve all substantive info; we could embed travel expectations in About the role or Responsibilities? Probably better to add a "Schedule" section with travel expectations? But schedule usually refers to work schedule (full-time, part-time, remote). Not present. The posting does not mention work hours. So we should not create Schedule section if no content. Travel expectations could be considered part of "About the role" or maybe "Responsibilities"? Not really. Could add a note in About the role. Compensation: "CompensationPay Range: USD $172,000-$215,000 (United States) / CAD $172,000-$220,000 (Canada)". That's Pay section. Hiring Journey etc is boilerplate? It's about process; could be considered boilerplate? It's not EEO etc but still process description. Might be considered boilerplate? The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Hiring journey is not exactly those but could be considered application instructions? It's describing interview process. Might be considered application instructions; better to drop. The instruction says remove application instructions. So we should drop the Hiring Journey section. Thus we need sections: About the role (prose), Responsibilities (list), Requirements (list), Qualifications (maybe same as Required Experience?), Skills (maybe Bonus Points?), Benefits (none explicit), Pay (compensation), Schedule (none). We need to decide mapping. We have Required Experience list (required). Bonus Points list (nice-to-have). Could map Required Experience to Requirements, Bonus Points to Skills. Qualifications maybe same as Requirements? Could combine. But we need to avoid thin sections. Perhaps we can have Requirements (required experience) and Skills (bonus points). Qualifications maybe not needed if we already have Requirements. But we need to include a section only when posting has real specific content. We have content for Requirements and Skills. Could also have Qualifications as same as Requirements but that would be duplicate. Better to have Requirements and Skills. Responsibilities: from "What You'll Own" list. About the role: combine initial paragraph plus maybe location and travel expectations. Pay: compensation. We must not invent or summarize away detail. Must preserve all substantive information. So we need to include all bullet points exactly as given (maybe rephrase? Must preserve info, not necessarily exact wording but cannot lose detail). We'll keep the substance. We need to format as HTML:
About the role
then...
. For lists:Responsibilities
- ... ...
Pay
Pay Range: USD $172,000-$215,000 (United States) / CAD $172,000-$220,000 (Canada)
. We need to ensure we don't include any markdown fences. Let's extract bullet points. Responsibilities (What You'll Own): - Assessment and plan: Evaluate our current pipelines, data architecture, and ML workflows, and produce a prioritized, opinionated plan for what needs to change. - Platform foundations and optimization: Own compute provisioning and environment management, keep training and serving environments reproducible, keep frameworks and packages current across services and model images, and tune latency, throughput, and spend, all without destabilizing production. - LLM infrastructure and smart routing: Build the layer our LLM features run on, including smart routing that sends each request to the cheapest model that can handle it well, plus the prompt and response evaluation needed to prove quality holds when we route down. - Experimentation and safe rollout: Give us a real discipline for A/B testing models before they are fully ramped: shadow deploys, canaries, holdouts, and success criteria agreed in advance, so a model earns its way into production instead of being switched on. - Observability and traceability: Know within minutes when a recommender goes down or starts drifting, and be able to explain why: model and data monitoring, alerting, regression detection, lineage, and enough traceability to reproduce a questionable recommendation on demand or trace a prediction back to the inputs that produced it (we currently use Braintrust; comparable tooling counts too). - Hands-on operations: Stay close enough to the running systems to operate them. You will work with the team to keep models online, but when something breaks in production you can dive in and help fix it, including models other people built. - Data and feature infrastructure: Own how features are computed, stored, and served consistently between training and inference, and take on the data engineering the team needs along the way. - Standards, not sole ownership: Establish the deployment and monitoring standards the rest of the team can run with. You are building shared ownership, not becoming the only person who keeps things online. Required Experience (list): - Staff-level, hands-on experience in MLOps, ML platform, or ML infrastructure (we're also open to Data Platform Engineer, ML Infrastructure Engineer, or Data Scientist backgrounds with strong platform ownership: the title on your last resume matters less than what you actually built) - Experience standing up MLOps practice end to end: CI/CD for models, experiment tracking, model registries, deployment workflows, and monitoring - Production experience with LLM-based systems: serving, prompt and response evaluation, routing across models and providers, and managing cost and latency tradeoffs. If you've done this with traditional ML systems and can show you pick up LLM tooling fast, that counts too - Experience operating models in both batch and real-time serving contexts - Hands-on with compute provisioning and environment management: containers, reproducible training and serving environments, and keeping frameworks and packages current on a fast-moving stack without breaking production - Experience running controlled model experiments in production: A/B tests, shadow or canary deploys, holdouts, and the judgment to set success criteria before ramping - Depth in observability and traceability for production ML: drift and regression detection, alerting that catches a recommender going down or going off the rails, lineage, and the ability to trace a prediction back to the inputs that produced it and reproduce it after the fact - Hands-on operational experience: you have carried the pager or its equivalent, debugged production ML incidents under time pressure, and fixed systems you did not originally build - Comfort doing the data engineering the platform needs: pipelines, feature computation and storage, and keeping training and serving features consistent - A track record of walking into complex, fast-grown systems, diagnosing the real problems, and materially improving them - Strong systems design ability: you can translate product needs into durable architecture and stay close enough to the code to build it yourself Bonus Points: - Interest in growing into model development yourself. This role starts on the platform side, but the line between platform and modeling is thin here, and we would rather hire someone who wants to cross it - Feature store experience. We are early here, so you would be shaping it rather than inheriting it - Experience evaluating AI/ML observability or LLM evaluation vendors, with judgment on when to buy versus build - Background in mission-driven, workforce, or government-adjacent data environments - Comfort mentoring a small data and engineering team while you build Tech Stack for Data (maybe could be under Skills? But we already have Skills as bonus points. Could add a separate Skills section for tech stack? The instruction: Use- for any list of duties/requirements/benefits. Tech stack is a list; could be considered Skills. But we already have Skills for bonus points. Could combine? Might be better to have a separate "Skills" section for tech stack, and Bonus Points as "Preferred Qualifications"? But we only have allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We could map Bonus Points to Qualifications, and Tech Stack to Skills. Or Requirements to Required Experience, Qualifications to Bonus Points, Skills to Tech Stack. Let's decide. We need to preserve all substantive info. So we need to include tech stack somewhere. Could be under Skills. Bonus points could be under Qualifications (preferred qualifications). That seems logical: Qualifications often include preferred qualifications. So: - Requirements: Required Experience (mandatory) - Qualifications: Bonus Points (preferred) - Skills: Tech Stack Thus we have sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Pay. Check if any Benefits: none explicit. So omit Benefits. Schedule: none explicit (travel expectations maybe but not schedule). We'll omit Schedule. Now we need to embed location and travel expectations in About the role paragraph. Let's craft About the role paragraph