machine learning engineer

ATG Entertainment

<div></div><div><div><div><ul><li><h4 class="jobSectionHeader">Salary</h4>
<div>$110,000k - $130,000k</div>
</li><li><h4 class="jobSectionHeader">Hours</h4>
<div>Various</div>
</li><li><h4 class="jobSectionHeader">Type</h4>
<div>Full Time</div>
</li><li><h4 class="jobSectionHeader">Location</h4>
<div>1419 Basin Street 
New Orleans 
Louisiana 
70116</div></li><li> 
<ul><li><div>No Files</div></li></ul>
</li></ul></div><div></div><div>POSITION: Machine Learning Engineer
LOCATION: Remote (San Antonio, Detroit, New Orleans)
HOURS: Full-Time, Non-Union. 40hrs/week
COMPENSATION: $110k - $130k (Commensurate with Experience)
ABOUT THE POSITION
As a Machine Learning Engineer, you will build production foundations that allow our machine learning capabilities to run reliably at scale across the US and UK. This role focuses on the engineering required to operationalize machine learning &mdash; including low‑latency serving, API integration, data pipelines for inference, monitoring, and robust deployment practices &mdash; so that model outputs can be activated in customer‑facing channels. 
You will deliver the real‑time serving layer that connects our data platform (e.g., Snowflake) with activation platforms such as Iterable. You will design and maintain the architecture, services, and workflows that move us from offline modelling to always‑on personalization, supporting CRM first and expanding to other channels over time. 
PEOPLE, PLACES, AND THINGS<ul><li>
Contribute to designing, scaling/building, evaluating, integrating, and refining machine learning models.
</li><li>Collaborate with Marketing and Engineering across the US and UK to define how machine learning underpins personalized customer communications.
</li><li>Build reusable science infrastructure, design and build production-grade measurement systems and establish robust evaluation frameworks.</li></ul> 
EXPERIENCE AND SKILLS
Essential<ul><li>
3+ years&rsquo; experience as an ML Engineer delivering production ML systems (models + pipelines) at scale.</li><li>
Degree in a quantitative field in Computer Science, Machine Learning, Operations Research (or equivalent technical background with demonstrated impact).</li><li>
Proficient in Python and modern ML tooling, with solid software engineering practices (testing, packaging, code quality).</li><li>
Hands‑on experience with AWS and cloud-native patterns (e.g., Lambda, S3, IAM/security, event-driven architectures, monitoring).</li><li>
Experience designing inference architectures (batch + low‑latency / near‑real‑time), including storage, caching, queues/streaming, and performance tuning.</li><li>
Familiar with orchestration and data pipelines for features/inference (e.g., Airflow) and modern data stacks (e.g., Snowflake/dbt), including API-based integrations.</li><li>
Familiar with MLOps foundations: model/version management, feature stores, monitoring/drift, and production experimentation (A/B assignment, logging, guardrails).</li><li>
Bonus: experience activating ML outputs in customer engagement platforms (e.g., Iterable) and real-time personalization patterns</li></ul>
BENEFITS<ul><li>
Medical, Dental and Vision Insurance
</li><li>401k Match</li><li>
Paid Vacation &amp; Holidays</li></ul> 
ATG Entertainment: Passion Behind Performance 
ATG Entertainment is a world leader in live entertainment. Our portfolio of venues includes historic theatres, studio theatres, cinemas, conference spaces, and modern live music arenas. ATG Entertainment own, operate or program 64 of the world&rsquo;s most iconic venues across the UK, the US and Germany entertaining over 18 million audience members each year. 
ATG Entertainment: IDEA Mission Statement
At ATG Entertainment, our commitment to inclusion, diversity, equity, and access (IDEA) is reflected in our IDEA mission statement: A stage for everyone. Our stages are a platform for compelling stories &ndash; stories that are for all, by all, and of all. We shine our spotlight on our differences and believe that understanding and celebrating these differences makes us better global citizens. We are committed to strengthening the sense of belonging by ensuring diversity and equity in everything we do. We strive to make our venues beacons of these ideals in our communities. Onstage and off, we hold ourselves accountable for nurturing an inclusive culture, one in which everyone can bring their authentic selves, and everyone feels they belong. At ATG Entertainment, we provide a stage for everyone.</div></div></div>

Machine Learning Engineer

Middesk

<div>
 
 About Middesk
 
 
 
 Middesk makes it easier for businesses to work together. Since 2018, we&rsquo;ve been transforming business identity verification, replacing slow, manual processes with seamless access to complete, up-to-date data. Our platform helps companies across industries confidently verify business identities, onboard customers faster, and reduce risk at every stage of the customer lifecycle.
 
 
 Middesk came out of Y Combinator, is backed by Sequoia Capital and Accel Partners, and was recently named to Forbes Fintech 50 List.
 
 
 
 The Role:
 
 
 
 We&rsquo;re building AI-driven applications that power business onboarding, fraud prevention, and identity verification. With proprietary data assets and deep domain expertise, we&rsquo;re uniquely positioned to create a new generation of ML-powered solutions for trust and risk.
 
 
 We&rsquo;re looking for a
 
 hands-on Machine Learning Engineer with strong Data Science expertise
 
 to take end-to-end ownership of the ML lifecycle: from feature design and model development, to deployment, monitoring, and iteration in production. Unlike larger organizations where responsibilities are split, you&rsquo;ll have the opportunity to
 
 own models from concept to production
 
 while working closely with product managers, engineers, and data platform teammates who support and amplify your work.
 
 
 This is a rare chance to join an
 
 earlier-stage company
 
 where you&rsquo;ll have broad visibility and influence, and where your ML systems will have immediate and measurable impact on customers.
 
 
 What You&rsquo;ll Do:
 
 
 <ul>
 <li>
 End-to-end ML ownership: Lead the full lifecycle of ML systems &mdash; feature engineering, model design, training, evaluation, deployment, monitoring, and iteration.
 </li>
 <li>
 Collaborate with a strong team: Work alongside data engineers, platform engineers, and product teammates who ensure you have the infrastructure, data, and context to deliver.
 </li>
 <li>
 Design &amp; deploy production models: Build high-performance ML applications in risk, fraud, trust &amp; safety, and compliance domains.
 </li>
 <li>
 Keep models healthy in production: Proactively monitor, detect drift, and retrain to ensure long-term performance and reliability.
 </li>
 <li>
 Experiment &amp; learn: Drive online experiments, offline evaluation, and counterfactual analyses to prove impact.
 </li>
 <li>
 Shape ML foundations: Contribute to the feature store, model management, training/serving pipelines, and best practices that scale ML across multiple use cases.
 
 
 </li>
 </ul>
 What We&rsquo;re Looking For:
 
 
 <ul>
 <li>
 4+ years applied ML experience with proven impact in risk, fraud, trust &amp; safety, compliance, fintech, or other high-stakes domains.
 </li>
 <li>
 Track record of owning ML models end-to-end &mdash; from research and design to deployment, monitoring, and retraining in production.
 </li>
 <li>
 Strong software engineering skills (Python, ML frameworks, deployment pipelines) and ability to write reliable, production-grade code.
 </li>
 <li>
 Hands-on experience with ML infrastructure such as feature stores, model management, training/serving pipelines, and monitoring tools.
 </li>
 <li>
 Comfortable as a senior IC: you can set technical direction, establish best practices, and mentor peers while collaborating effectively across teams.
 </li>
 <li>
 Experience working cross-functionally with data engineers, platform engineers, and product stakeholders to bring ML systems to life.
 </li>
 <li>
 Deep expertise in classification challenges such as imbalanced labels, sparse signals, cold start, and production version management.
 
 
 </li>
 </ul>
 Nice to Haves:
 
 
 <ul>
 <li>
 B2B SaaS experience, ideally building ML products for enterprise customers.
 </li>
 <li>
 Familiarity with graph, LLM-based feature generation, or AI agent workflows.
 </li>
 <li>
 Experience scaling ML across multiple products or risk domains.
 </li>
 </ul>
</div>

Stealth Startup

<div>
 
 The Culture We Are Building
 
 <ul>
 <li>
 We are assembling a lean group of generalists. While we welcome experience, we're primarily looking for creative, curious problem solvers with a keen interest in open-ended, technically challenging problems.
 </li>
 <li>
 We are looking to hire in-person in San Francisco. We believe that having a community is critical for learning, growth, and camaraderie. We will pay generously for relocation assistance.
 </li>
 <li>
 We want to invest heavily in our team's growth. We will happily sponsor conference attendance and encourage projects and research. If there's something that will help you grow as an engineer, researcher, or designer, we actively want to support it.
 </li>
 <li>
 We believe in compensating exceptional talent exceptionally well. We are an early stage company, but are committed to working with candidates we're excited about to ensure they are excited too.
 </li>
 <li>
 We have an academic as well as competitive spirit. We enjoy thought exercises, math puzzles, intellectual inquiry, and vigorous debates.
 </li>
 </ul>
 
 
 
 
 Role: Machine Learning Engineer
 
 
 Design, train, and ship models that guide reviewers to the most important changes, resolve merge conflicts more safely, and make diffs understandable through semantic grouping and summarization. You'll combine retrieval, code‑aware modeling, and small‑model techniques to deliver low‑latency, cost‑effective ML in production.
 
 
 Responsibilities
 
 <ul>
 <li>
 -Train and fine‑tune models that predict the next location a reviewer should focus on: ranking files, hunks, and comments
 </li>
 <li>
 -Build ML‑assisted merge conflict resolution: align and reconcile hunks using code context/AST, propose safe resolutions, and explain changes
 </li>
 <li>
 -Develop semantic change grouping and diff visualization: detect refactors/moves, cluster related edits across files, and generate concise summaries
 </li>
 <li>
 -Evaluate retrieval and RAG for pull requests: retrieve relevant context, discussions, and history to improve grouping, prioritization, and summarization
 </li>
 <li>
 -Design offline/online evaluation: labeled datasets, ranking metrics (e.g., MAP/NDCG), A/B experiments, and human‑in‑the‑loop feedback
 </li>
 <li>
 -Operate efficient inference: apply small‑model techniques (LoRA, supervised fine‑tuning, distillation, quantization)
 </li>
 </ul>
 
 Qualifications
 
 <ul>
 <li>
 -Strong background in deep learning research and implementation, with experience in modern architectures and concepts (transformers, MoE, reasoning, etc.)
 </li>
 <li>
 -Track record publishing results at top-tier conferences (NeurIPS, ICML, ICLR)
 </li>
 <li>
 -Expertise in Python and ML frameworks (PyTorch, JAX)
 </li>
 <li>
 -Experience with MLOps and model optimization (ONNX, TensorRT, serving infrastructure)
 </li>
 <li>
 -Familiarity with cost‑effective techniques: LoRA, supervised fine‑tuning, distillation, and quantization for small/efficient models
 </li>
 <li>
 -Familiarity with systems programming across the stack: Rust/C/C++, CUDA/GPU optimization, and kernel-level performance tuning
 </li>
 <li>
 -Experience with reinforcement learning, particularly in systems optimization and decision-making tasks
 </li>
 </ul>
 
 
</div>

Machine Learning Engineer [32985]

Capital One

<div>Lead Machine Learning Engineer
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You&rsquo;ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You&rsquo;ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
What you&rsquo;ll do in the role:
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:<ul><li>
Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.</li><li>
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).</li><li>
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.</li><li>
Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.</li><li>
Retrain, maintain, and monitor models in production.</li><li>
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</li><li>
Construct optimized data pipelines to feed ML models.</li><li>
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.</li><li>
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.</li><li>
Use programming languages like Python, Scala, or Java.</li></ul>
Basic Qualifications:<ul><li>
Bachelor&rsquo;s Degree</li><li>
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)</li><li>
At least 4 years of experience programming with Python, Scala, or Java</li><li>
At least 2 years of experience building, scaling, and optimizing ML systems</li></ul>
Preferred Qualifications:<ul><li>
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field</li><li>
3+ years of experience building production-ready data pipelines that feed ML models</li><li>
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow</li><li>
2+ years of experience developing performant, resilient, and maintainable code</li><li>
2+ years of experience with data gathering and preparation for ML models</li><li>
2+ years of people leader experience</li><li>
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation</li><li>
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</li><li>
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance</li><li>
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents</li></ul>
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer 

New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer 
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</div>

Lead Machine Learning Engineer

Handshake

<div><h1 class="jobSectionHeader">About Handshake</h1>
Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.
Why join Handshake now:<ul><li>
Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel</li><li>
Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world&rsquo;s top educational institutions</li><li>
Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others</li><li>
Build a massive, fast-growing business with billions in revenue</li></ul><h1 class="jobSectionHeader">
About the Role
</h1>Handshake is hiring an Associate Machine Learning Engineer for the Growth Relevance team. AI is transforming how students navigate their careers, and we're committed to providing innovative, responsible AI-powered solutions that guide students from educational aspirations to meaningful career opportunities. In this role, you will contribute directly to this mission by developing, deploying, and enhancing machine learning systems focused on lifecycle optimization, personalized notifications, and monetization strategies.
You'll join a high-impact team leveraging cutting-edge ML infrastructure, including embedding-based retrieval, Graph Neural Networks, and multi-stage rankers built upon a robust data platform with billions of data points. Your work will drive critical marketplace metrics, enhance user engagement, and contribute to responsible AI practices around explainability, fairness, and quality.
What You'll Do<ul><li>
Innovator: Develop and iterate on machine learning models and features that directly influence user experience across lifecycle, notifications, and monetization &mdash; with guidance from senior engineers.</li><li>
Collaborator: Partner with senior engineers, data scientists, and product managers to develop and iterate on machine learning models that improve product features and user experience.</li><li>
Learner: Grow your technical depth by working alongside experienced ML practitioners, picking up best practices in model development, experimentation, and production deployment.</li></ul><h1 class="jobSectionHeader">
Desired Capabilities
</h1><ul><li>Bachelor&rsquo;s degree in Computer Science, Data Science, or a related field</li><li>
0&ndash;2 years of experience in machine learning, data science, or a related area</li><li>
Proficient in Python, with hands-on experience in frameworks such as scikit-learn, PyTorch, or TensorFlow</li><li>
Strong foundation in core ML concepts, including classification, regression, ranking, and model evaluation</li></ul>
Extra Credit<ul><li>
Master&rsquo;s degree or currently pursuing an advanced degree in a relevant field</li><li>
Exposure to areas such as recommendations, personalization, NLP, deep learning, LLMs, or explainable AI</li><li>
Familiarity with the ML lifecycle (e.g., experiment tracking, model monitoring, feature pipelines)</li><li>
Experience with cloud platforms (GCP, AWS, or Azure)</li><li>
Clear communicator, able to translate technical work for diverse audiences</li><li>
Collaborative mindset with experience working cross-functionally with product, analytics, and engineering teams</li></ul><h1 class="jobSectionHeader">
Perks</h1>
Handshake delivers benefits that help you feel supported&mdash;and thrive at work and in life.
The below benefits are for full-time US employees.
Ownership: Equity in a fast-growing company
Financial Wellness: 401(k) match, competitive compensation, financial coaching
Family Support: Paid parental leave, fertility benefits, parental coaching
Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
Growth: $2,000 learning stipend, ongoing development
Remote &amp; Office: Internet, commuting, and free lunch/gym in our SF office
Time Off: Flexible PTO, 15 holidays + 2 flex days
Connection: Team outings &amp; referral bonuses
Explore our mission, values, and comprehensive US benefits at joinhandshake.com/careers.
Compensation Range: $115K - $144K</div>

Associate Machine Learning Engineer

Hike Medical

<div>The Role 
As a Senior Machine Learning Engineer, you will bridge the gap between raw computer vision and physical, 3D-printed reality. You will own the pipeline that translates a smartphone video into a high-fidelity, CAD-compatible 3D reconstruction of human anatomy. 
What You&rsquo;ll Work On<ul><li>
3D Reconstruction &amp; Generation: Develop and deploy SOTA models for 3D human body part reconstruction (SDFs, NeRFs, Gaussian Splatting, or Mesh-based approaches) from videos.</li><li>
Deep Learning: Architect pipelines that handle non-Euclidean data to extract precise clinical measurements from noisy real-world scans.</li><li>
CAD Integration: Bridge the gap between AI inference and parametric CAD modeling for automated 3D printing preparation.</li><li>
Rapid Prototyping &amp; Continual Learning: You should be used to keeping up to date on latest advances and quickly implementing these advancements into our systems.</li><li>
Production ML: Work with our engineering team to deploy models into our AWS/serverless environment, ensuring clinical-grade accuracy at scale.</li></ul> 
Ideal Candidate Profile 
Technical Requirements<ul><li>
PhD in Machine Learning, Computer Graphics, Geometry Processing, or a related field, or equivalent experience.</li><li>
Deep expertise in 3D representations (Meshes, Point Clouds, Voxels, SDFs) and their respective computational tradeoffs.</li><li>
Proven track record in Generative Vision, 3D Reconstruction, or related topics.</li><li>
Strong proficiency in Python and PyTorch.</li><li>
Experience in CAD and computational geometry is a big plus.</li><li>
Experience with ML infrastructure in AWS (SageMaker, Lambda, etc) and model deployment is a plus.</li><li>
Experience in Biomedical AI or Biomechanics (e.g., modeling joint movement or tissue) is highly valued but not required.</li></ul> 
Helpful Characteristics<ul><li>
Fast Iteration Mindset: Willingness to move fast, iterate confidently in ambiguous tasks and deliver results from imprecise instructions or requirements.</li><li>
Product Minded: Our AI team communicates the art of the possible. Having a good eye for high-value product opportunities is a major plus.</li><li>
Polymathic: You can discuss loss functions with researchers, API latency with engineers, and 3D printing constraints with manufacturing.</li><li>
Strong Communicator: You clearly explain your ideas, challenge assumptions, and collaborate across engineering, AI, and product teams.</li><li>
Mission-Driven: You want to build technology that helps people walk without pain and revolutionize the future of orthopaedic care.</li><li>
Thrives in Collaboration: You push back, ask tough questions, and elevate the team.</li></ul></div>

Senior Machine Learning Engineer

MANTECH

<div>MANTECH seeks a highly skilled and passionate AI/Machine Learning Engineer to join our innovative technology team. In this role, you will help bridge the gap between research and engineering by designing, building, and deploying machine learning solutions that address complex mission challenges. You will contribute to the development of generative AI applications, enhance existing pipelines, and support the growth of junior engineers. 
Responsibilities include, but are not limited to:<ul><li>
Model Development: Design, develop, and train advanced machine learning and deep learning models, including LLMs, CNNs, or Transformers, tailored to specific business applications.</li><li>
Evaluation &amp; Monitoring: Develop metrics to evaluate model performance and monitor deployed models to prevent decay or drift.</li><li>
Collaboration: Work closely with program areas and software engineers to integrate AI capabilities into our core products.</li></ul> 
Minimum Qualifications:<ul><li>
Bachelor&rsquo;s degree in Computer Science, Mathematics, or a related technical field, or High School Diploma and 4+ years of relevant experience</li><li>
Experience programming in Python</li><li>
Experience developing and training machine learning or deep learning models</li><li>
Understanding of modern ML frameworks and tools</li><li>
Understanding of model evaluation, validation, and monitoring techniques</li></ul> 
Clearance Requirements:
<ul><li>Must have an active/current TS/SCI with polygraph clearance</li></ul> 
Physical Requirements:<ul><li>
Must be able to remain in a stationary position at least 50% of the time.</li></ul></div>

AI Machine Learning Engineer

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