Machine Learning Engineer (ml Ops)

Our Mission Headquartered out of Denver, CO, we’re a small but mighty team on a mission to be the best and easiest way to learn at work.

We imagine a world where learning happens in the flow of work. Where employees maximize the minutes of their lives. Where knowledge is contextual, personalized and instantly accessible. Where learning at work is as easy and joyful as it is in our personal lives. This is the future we’re building at Spekit.

Our Product Say goodbye to distracted zoom training sessions and lengthy LMS courses your teams will forget. Spekit is the leading just-in-time enablement platform that meets your reps when and where they need it, in the tools they use every day.

Spekit takes all of your training & enablement - for applications, processes, sales playbooks, SOPs and more and embeds that training directly in your employees’ tools & workflows. Think of Spekit as your employee’s digital sidekick that delivers real-time, personalized enablement in their flow of work™. Our unified enablement platform prioritizes three pillars: content, user experience, and flexibility. We focus on delivering the right answer, at the right time – all within a streamlined and intuitive interface. No more information overload, no more hunting for answers. That's Simple, yet Spektacular.

With over $60M in VC funding from Bonfire Ventures, Matchstick Ventures, The Foundry Group, Renegade Partners, The Operator Collective and other top VCs, thousands of employees from scaling startups to Fortune 400 organizations leverage Spekit to onboard new hires, facilitate change management and drive adoption of their tools and applications.

Location: Strongly preferred Denver, CO or the surrounding Denver area. Open to remote US locations ONLY in the following states: CA, CO, MA, MI, NC, NM, NV, NY, OH, TX, UT, WA, WI. We will not be sponsoring visa applications at this time nor accepting resumes from locations outside of these states or outside of the US.

What Are We Building? We view the user’s “context” as the app they’re using, the page/URL they’re on, and the task they need to accomplish. Our aim is to harness this context and combine it with the resources our customers upload and input, allowing their teams to be far more effective: from closing sales faster to shortening response times on support tickets. Our unique advantage is how closely we can embed ourselves into each user’s workflow.

In the coming year, we’ll be:

Recommending Content: Building intelligence that suggests the most relevant information, resources, or assets at the right time. Improving Search & Retrieval: Enhancing our AI-powered retrieval-augmented generation (RAG) pipeline and surfacing answers through a conversational chat interface. Optimizing User Workflows: Instrumenting and refining the experience so that content creation, curation, and consumption form a virtuous cycle. Predicting Needs: Advancing personalization by predicting what information or resource an individual (or team) will need next, reducing friction and accelerating outcomes.

Why Are We Hiring a Machine Learning Engineer? We’re building a small, high-impact ML team that tackles problems with an iterative and experimental mindset. We’re primarily looking for a candidate that has significant experience in an MLOps role. You’ll work alongside our existing ML engineer to design and deploy solutions that leverage user context and customer-provided content to improve productivity and outcomes. We look for individuals who:

#1: Are pragmatic enough to understand trade-offs #2: Use data to drive decisions #3: Aren’t afraid of experimentation and learning from failures #4: Are passionate about delivering value to customers

🎥 About the role Hear it directly from our Hiring Manager, Adam Rosson!

If that sounds like you, keep reading!

What You’ll Do

Enhance & Scale ML Pipelines

• Work closely with our existing ML engineer to refine and scale our retrieval-augmented generation (RAG) pipeline that leverages real-time web page data.
• Ensure our machine learning infrastructure is resilient, scalable, and supports rapid experimentation.

Model Development & Deployment

• Prototype and tune NLP/LLM pipelines to deliver personalized recommendations and support conversational interfaces.
• Deploy pipelines to production and monitor their performance, continuously optimizing for speed and accuracy.

Data Processing & Feature Engineering

• Clean, preprocess, augment, and validate datasets (both structured and unstructured)

Research & Innovation

• Stay up to date on cutting-edge AI/ML trends, particularly around LLMs, embeddings, vector search, and deep learning architectures.
• Experiment with new frameworks, libraries, and architectures to keep Spekit at the forefront of innovation

Cross-Functional Collaboration

• Partner with product managers and other teams to understand user needs and deliver ML solutions that directly impact end-user outcomes.
• Communicate technical concepts to non-technical stakeholders, ensuring alignment and shared understanding

Culture & Mentorship

• Model best practices, but also know how to make tradeoffs based on the situation.
• Mentor team members, contribute ideas, and foster a collaborative, learning-focused environment

Skills & Qualifications: Significant Experience in MLOps

• Model evaluation and explainability
• Model version tracking & governance
• Creating and using benchmarks, metrics, and monitoring to measure and improve services
• Providing best practices and executing POC for automated and efficient model operations at scale
• Designing and developing scalable MLOps frameworks to support models based on requirements
• Collecting, cleaning, and labeling data for improving AI pipelines and testing
• Integrating model development with CI/CD practices

Analytical & Problem-Solving

• Strong background in probability, statistics, and algorithmic thinkingComfortable exploring multiple solutions and weighing trade-offs

Technical Proficiency

• Expertise in Python (and ML libraries)
• Experience with NLP, neural networks, deep learning architectures, and related frameworks
• Experience with evaluating RAG pipelines
• Experience with Jupyter notebooks, Google Collab, or similar tool
• Experience with Haystack and LangChain
• Proven ability to build, train, tune, and deploy models in production environments
• Understanding of data structures, data modeling, and software architecture

Experimentation & Optimization

• Hands-on experience with A/B testing and model optimization approaches
• Ability to analyze performance metrics and iterate quickly

Soft Skills & Mindset

• Excellent time management and organizational skills
• Comfortable managing ambiguity and driving projects to clarity
• Eagerness to learn, adapt, and experiment

The Ideal Candidate

• Proficiency in MLOps: Productionalizing our MLOps for observability, rapid experimentation, debugging pipelines for quality and performance.  Knowledge of graph databases is welcome, but not necessary.
• Knows Best Practices but Adapts: Knows when standard methodologies apply—and when to flex them for the situation.
• Outcome-Oriented: Prioritizes business and customer impact over personal preference for specific tools or technologies.
• Self-Sufficient: Can navigate unstructured problems and seek clarity but doesn’t wait for everything to be perfectly defined.
• Collaborative: Values teamwork, sees feedback and cross-functional engagement as beneficial rather than a bottleneck.
• ersatile: Loves wearing multiple hats and stepping into different roles to fill knowledge gaps as projects demand.
• Passionate: Truly enjoys the craft of machine learning, sharing their excitement, and helping teammates grow.

Who we're not looking for

• Tool or Technique-Obsessed: You care more about using a hot new technology than delivering real customer value.
• Highly Specialized & Rigid: You resist learning new skills and prefer to stay in a narrow comfort zone.
• Needs Everything “Ready-Made”: You struggle in environments where processes and structures are still evolving.
• Solo Operator: You find collaboration inefficient and prefer working entirely on your own.
• Change-Averse: You can’t easily pivot as priorities shift in a fast-paced startup environment.