learning engineer

Booz Allen Hamilton

<div>Machine Learning Engineer
The Opportunity:
As a programmer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques makes you an integral part of delivering a customer-focused solution. We need your technical knowledge and desire to problem-solve to support warfighter performance challenges. As a machine learning engineer on our Warfighter Performance team, you&rsquo;ll train, test, deploy, and maintain models that learn from data.
In this role, you&rsquo;ll contribute to the direction of mission-critical solutions by applying best-fit ML algorithms and technologies. You&rsquo;ll grow within a large community of machine learning engineers across the company and collaborate with data engineers, data scientists, solutions architects, SMEs, and product owners to deliver world-class solutions to optimize warfighter performance. Your skills and technical experience will help your clients navigate the landscape of ML algorithms, tools, and frameworks.
Work with us to solve real-world challenges and define ML strategy for warfighter performance and warfighter technology.
Join us. The world can&rsquo;t wait.
You Have:<ul><li>
Experience with Python, SQL, or Git</li><li>
Experience with data analysis or machine learning workflows and models</li><li>
Experience using libraries such as pandas, NumPy, scikit-learn, PyTorch, or TensorFlow</li><li>
Experience building scripts, notebooks, prototypes, or small applications that process data and support AI/ML use cases</li><li>
Knowledge of machine learning fundamentals, data preprocessing, model evaluation, prompt engineering, LLMs, embeddings, and retrieval-augmented generation concepts</li><li>
Knowledge of APIs, structured data, cloud-hosted services, and how AI systems use tools and external knowledge sources to generate grounded responses</li><li>
Ability to write clean code, troubleshoot technical issues, learn new frameworks quickly, and support development of AI/ML prototypes, data pipelines, model workflows, and agentic workflow components</li><li>
Ability to communicate technical work clearly, document progress, and present findings to technical and non-technical stakeholders</li><li>
Ability to obtain a Secret clearance</li><li>
Bachelor&rsquo;s degree</li></ul>
Nice If You Have:<ul><li>
Experience supporting LLM-based applications, retrieval-augmented generation, tool-calling agents, evaluation workflows, or AI-assisted decision support systems</li><li>
Experience with cloud or DevOps tools such as AWS, Docker, CI/CD, Linux, APIs, vector search, notebooks, lightweight dashboards, or backend services</li><li>
Knowledge of agentic AI patterns, including tool use, prompt libraries, memory, retrieval, guardrails, evaluation, and human-in-the-loop workflows</li><li>
Knowledge of human performance, health, fitness, sleep, nutrition, readiness, or behavioral data</li><li>
Ability to help design, test, and improve agentic AI workflows that combine models, tools, data sources, and business rules</li><li>
Ability to document experiments, compare model outputs, evaluate failure cases, and clearly brief recommendations, risks, and progress to project teams or clients</li><li>
Secret clearance</li><li>
Master&rsquo;s degree</li></ul>
Clearance:
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; Secret clearance is required.
Compensation
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen&rsquo;s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual&rsquo;s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $77,600.00 to $176,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen&rsquo;s total compensation package for employees. This posting will close within 90 days from the Posting Date.
Identity Statement
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Candidate AI Usage Policy
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided.
Work Model 
Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.<ul><li>
Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.</li><li>
Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.</li><li>
Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.</li></ul>
Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.</div>

Machine Learning Engineer

The Hartford

<div><h2 class="jobSectionHeader">Job Details</h2>
<div><div><div>Location:</div>
<div>Chicago, IL</div>
</div><div><div>Category:</div><div>
Data &amp; Analytics</div>
</div><div><div>Employment Type:</div>
<div>Full time
</div></div><div><div>Job Ref:</div><div>
R2625795-172</div>
</div></div><div><div>Data Engineer - GE08AE
We&rsquo;re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals &ndash; and to help others accomplish theirs, too. Join our team as we help shape the future. 
<div>The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. 
The Hartford is developing industry‑leading AI and machine learning capabilities to improve the various facets of the Global Specialty underwriting experience. On the Global Specialty Applied AI team, we utilize the latest AI products and frameworks to accelerate the processes that our partners touch day to day and advance the speed and intelligence with which we make our decisions. As a Machine Learning AI Engineer, you will play a key role in contributing to the designing, building, and operationalizing production‑grade AI solutions&mdash;partnering closely with product, engineering, and platform leaders to deliver measurable impact. 
 
Our core values 
<ul><li>We build AI solutions, not models. We are thoughtful in supporting the end-to-end business problem, with an eye to systems design.</li><li>We are trusted and transparent. We collaborate tightly with our partners and are mindful of their capacity to absorb change.</li><li>We provide assets that are safe to buy. Our products are delivered with a full monitoring solution to ensure our products continue to deliver as expected.</li><li>We will earn the right to influence. With humble confidence, we listen carefully to learn from our customers and become partners in problem solving.</li><li>We are practical and evolutional. We first deliver a minimally viable product and over time expand its sophistication based on feedback.</li></ul> 
Responsibilities 
<ul><li>Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.</li><li>Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.</li><li>Accountable for deployment design, development and maintenance of both traditional ML and AI models.</li><li>Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams</li><li>Delivery of critical milestones for model deployment in the AWS and GCP cloud environments.</li><li>Adopt and promote MLOps best practices to the Data Science community.</li></ul> 
Minimum Requirements 
<ul><li>Must be authorized to work in the U.S. now and in the future.</li><li>1+ years of equivalent experience in a research or DevOps function.</li><li>Development experience developing solutions within AWS, GCP or both.</li><li>Exposure to developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.</li><li>Familiarity with building and deploying API services within the Cloud.</li><li>Familiarity building CICD pipelines using Jenkins or equivalent</li><li>Exposure with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or equivalents</li><li>Experience in Unix, git, and strong object oriented development experience using Python</li><li>Exposure to with workflow automation platforms (Apache Airflow, Autosys, similar)</li><li>Basic understanding of Data Science model development life cycle</li></ul></div><ul><li>Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM&rsquo;s into automated processes</li></ul>
This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).
Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford&rsquo;s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$100,960 - $151,440
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age</div></div></div>

AI Machine Learning Engineer

MultiBank Group

<div>Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.
Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.
Role Overview
We are seeking a Senior Machine Learning Engineer to join our AI team as a technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle &mdash; from model design through deployment, optimization, and ongoing performance in production &mdash; and contributes to the technical direction of the AI platform.
Key Responsibilities<ul><li>
Architect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day one</li><li>
Build and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scale</li><li>
Own technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainability</li><li>
Design and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applications</li><li>
Implement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelines</li><li>
Continuously optimize model performance, inference latency, cost efficiency, and reliability across live systems</li><li>
Collaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutions</li><li>
Mentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the team</li><li>
Contribute to strategic decisions around data architecture, AI infrastructure, and cloud platform direction</li><li>
Work with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimization</li></ul>
Requirements<ul><li>
7 to 15 or more years of experience in software engineering, data science, or ML engineering</li><li>
Strong background in product companies, scale-ups, or enterprise AI platforms</li><li>
Proven track record of building production-grade AI systems, not solely notebooks or proof-of-concept work</li><li>
Comfortable owning systems end-to-end from data through model through deployment through monitoring</li><li>
Product-first engineering approach, not research-only profiles</li><li>
Advanced Python engineering skills with strong systems thinking and a focus on production quality</li><li>
Comfortable with fast iteration cycles and deploying models into live environments</li><li>
Ability to work directly and confidently with stakeholders and product owners</li><li>
Fintech or financial services experience is an advantage</li></ul>
Technical Skills
Machine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)
LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelines
MLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacks
Cloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCP
Data Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architectures
Why Join Us?<ul><li>
Work with one of the world&rsquo;s leading financial derivatives institutions.</li><li>
Competitive salary plus performance-based incentives.</li><li>
Access to a dynamic, international, and fast-growing environment.</li><li>
Strong opportunities for career progression within a global financial group.</li><li>
Be part of a business committed to innovation, excellence, and long-term growth.</li></ul>
Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.
MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.</div>

Senior Machine Learning Engineer

Motorola Solutions

<div><h2 class="jobSectionHeader">Company Overview
</h2>At Motorola Solutions, we believe that everything starts with our people. We&rsquo;re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that&rsquo;s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future. 
<h2 class="jobSectionHeader"> 
Department Overview
</h2>Silvus Technologies is dedicated to one mission: connecting those who keep us safe. We do so by delivering the most advanced Mobile Ad-hoc Network (MANET) radios powered by our custom and ever evolving Mobile-Networked MIMO waveform. Together, our radios and waveform provide vital communications for mission-critical applications in the harshest environments from underground tunnels to high-altitude balloons.
 
 
Silvus StreamCaster&trade; radios are being rapidly adopted by customers all over the world ranging from the U.S. and Allied Nations Departments of Defense to International, Federal, State, and Local Law Enforcement agencies, all the way to the Superbowl, Grammys, and industry-leading drone, robot, and unmanned systems manufacturers.
 
 
We&rsquo;re excited about the work we are doing in AI. Founded on our many years of experience and knowledge, we know the mission-critical needs of our customers are unique and different from the consumer technologies that leverage AI today.
 
 
Silvus Technologies is a wholly owned subsidiary of Motorola Solutions, Inc.
<h2 class="jobSectionHeader"> 
Job Description
</h2>Would you like to join a talented group of people, doing challenging work, with the mission of &ldquo;Keeping Our Heroes Connected&rdquo;?
You will join the Signals and Systems group where you will be developing machine learning solutions that enhance performance in wireless communication systems. Your work will directly impact our customers in the form of products and services that make use of signal processing technology. Unlike similar roles in the industry, the result of your work has the potential to impact the entire lifecycle of the workflows impacting people's lives in moments that matter.
<h2 class="jobSectionHeader">Roles and Responsibilities
</h2><ul><li>Manage inputs gathered from unusual sources, including captures from software defined radio (SDR) over a wide range of RF signals
</li><li>Combine knowledge of signal processing, probability and statistics, machine learning, and modern methods of artificial intelligence to build large-scale and high-throughput systems handling vast quantities of data
</li><li>Collaborate with UX designers, infrastructure engineers, and other research scientists to develop prototypes and integrate ML algorithms that work across a wide range of scales from resource-constrained edge compute to full-sized data centers
</li><li>Stay current with the latest machine learning research for wireless and embedded systems, applying ingenuity and a deep understanding of the problems at hand
</li></ul><h2 class="jobSectionHeader">Required Skills
</h2><ul><li>6+ years experience as a machine learning engineer
</li></ul><ul><li>Expert knowledge in Python and PyTorch or TensorFlow
</li><li>Strong foundation in supervised and unsupervised learning and statistical modeling
</li><li>Strong mathematics background, particularly in linear algebra and probability
</li><li>Strong written and oral communication skills
</li></ul><h2 class="jobSectionHeader">Desired Skills
</h2><ul><li>Advanced degree in a quantitative field such as electrical and computer engineering, physics, mathematics or statistics
</li><li>Familiarity with relational and NoSQL databases
</li><li>Familiarity with RF signal processing and SDR for signals intelligence or electronic warfare
</li><li>Familiarity with cloud-based infrastructure: Azure and/or AWS
</li><li>Experience tracking projects with Jira, Azure DevOps or similar tooling
</li><li>Experience with Linux, DevOps (command line)
</li><li>Experience with containerized infrastructure (Docker, Kubernetes)
</li><li>Familiarity with regulated environments, such as sovereign clouds
</li></ul>Target Base Salary Range: $195,000 USD - $225,000 USD
Consistent with Motorola Solutions values and applicable law, we provide the following information to promote pay transparency and equity. Pay within this range varies and depends on job-related knowledge, skills, and experience. The actual offer will be based on the individual candidate. 
 
As a part of the application process, we encourage you to include a link to code you&rsquo;ve written.
 
 
Note: This role is 100% on-site in our Los Angeles office. 
 
#LI-MP2
 
#LI-ONSITE 
<h2 class="jobSectionHeader"> 
Basic Requirements
</h2><ul><li>Bachelor degree with 8+ years experience as a machine learning engineer
</li><li>AND 6+ years of Python and PyTorch or TensorFlow experience
</li><li>Must be a U.S. citizen with the ability to obtain necessary security clearance as required by government contract. 
</li></ul><h2 class="jobSectionHeader"> 
Travel Requirements
</h2>None
<h2 class="jobSectionHeader"> 
Relocation Provided
</h2>None
<h2 class="jobSectionHeader"> 
Position Type
</h2>Experienced
<h2 class="jobSectionHeader"> 
Referral Payment Plan
</h2>No
Our U.S. Benefits include:
<ul><li>Incentive Bonus Plans
</li><li>Medical, Dental, Vision benefits
</li><li>401K with Company Match
</li><li>10 Paid Holidays
</li><li>Generous Paid Time Off Packages
</li><li>Employee Stock Purchase Plan
</li><li>Paid Parental &amp; Family Leave
</li><li>and more! 
</li></ul> 
EEO Statement
Motorola Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or belief, sex, sexual orientation, gender identity, national origin, disability, veteran status or any other legally-protected characteristic.
We are proud of our people-first and community-focused culture, empowering every Motorolan to be their most authentic self and to do their best work to deliver on the promise of a safer world. If you&rsquo;d like to join our team but feel that you don&rsquo;t quite meet all of the preferred skills, we&rsquo;d still love to hear why you think you&rsquo;d be a great addition to our team.
We&rsquo;re committed to providing an inclusive and accessible recruiting experience for candidates with disabilities, or other physical or mental health conditions. To request an accommodation, please complete this Reasonable Accommodations Form so we can assist you.</div>

Paramount

<div>
 
 #WeAreParamount on a mission to unleash the power of content&hellip; you in?
 
 
 
 We&rsquo;ve got the brands, we&rsquo;ve got the stars, we&rsquo;ve got the power to achieve our mission to entertain the planet &ndash; now all we&rsquo;re missing is&hellip; YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter &ndash; both for our audiences and our employees &ndash; and aim to leave a positive mark on culture.
 
 
 
 Overview
 
 
 
 As a member of the Applied Machine Learning Group, you&rsquo;ll help build a world-class streaming experience within the team. Your mission is to get the user watching. You own the "onboarding" and "re-entry" experience, including high-commitment surfaces like Your Next Watch (YNW), Jump Back In (JBI), and While You Were Away (WYWA).
 
 
 You&rsquo;ll put customers first by using MLFlow for experiment tracking, Qdrant for retrieval, and Post-training RL to ensure that the content we surface at the start of a session is what the user is most likely to commit to. You will be in charge of implementing the components that solve the "Cold Start" problem for new users and provide seamless re-entry for returning ones. You&rsquo;ll work in an IC2 (Mid-level) role to ensure these surfaces, which optimize for Start Rate under uncertainty, identify if a user stays or leaves within the first 30 seconds of app launch.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Independent Delivery: Own the implementation of features for JBI (Jump Back In) and YNW (Your Next Watch).
 </li>
 <li>
 Retrieval Optimization: Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends.
 </li>
 <li>
 Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a high bar for reproducibility.
 </li>
 <li>
 Model Training: Develop and serve models in GCP using TensorFlow/PyTorch, incorporating Post-training RL for reward-based optimization.
 </li>
 <li>
 Collaborative Quality: Participate in design reviews to ensure your components share the same high-reliability standards as the rest of the pod.
 </li>
 <li>
 Solving Cold Start: Build the logic that makes the app feel personalized even for users we know very little about.
 </li>
 <li>
 High-Commitment Accuracy: Optimize for the "Play" button&mdash;the ultimate signal of user commitment.
 
 
 </li>
 </ul>
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 3+ years in MLE.
 </li>
 <li>
 Experience with GCP and MLFlow.
 </li>
 <li>
 Proficiency in TensorFlow/PyTorch.
 </li>
 <li>
 Knowledge with Vector DBs.
 </li>
 <li>
 Proven ability to implement features that solve the "Cold Start" problem for new users.
 </li>
 <li>
 Experience managing, tracking, and deploying experiments to ensure a high bar for reproducibility.
 
 
 </li>
 </ul>
 Bonus Skills
 
 
 <ul>
 <li>
 Background in multi-stage ranking or "Cold Start" problems.
 </li>
 <li>
 Experience with retrieval optimization and re-entry carousels.
 </li>
 <li>
 Knowledge with Post-training RL for reward-based optimization.
 
 
 </li>
 </ul>
 Paramount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment&trade;. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME&reg;.
 
 
 
 Additional Information
 
 
 
 Hiring Salary Range: $130,200.00 - 195,300.00.
 
 
 The hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO or, if applicable, as otherwise dictated by the appropriate Collective Bargaining Agreement. This position is bonus eligible.
 
 
 
 What We Offer:
 
 
 
 <ul>
 <li>
 Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here: https://www.paramount.com/careers/benefits
 </li>
 <li>
 Generous paid time off.
 </li>
 <li>
 An exciting and fulfilling opportunity to be part of one of Paramount&rsquo;s most dynamic teams.
 </li>
 <li>
 Opportunities for both on-site and virtual engagement events.
 </li>
 <li>
 Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
 </li>
 <li>
 Explore life at Paramount: https://www.paramount.com/careers/life-at-paramount
 
 
 </li>
 </ul>
 Paramount is an equal opportunity employer (EOE) including disability/vet.
 
 
 At Paramount, the spirit of inclusion feeds into everything that we do, on-screen and off. From the programming and movies we create to employee benefits/programs and social impact outreach initiatives, we believe that opportunity, access, resources and rewards should be available to and for the benefit of all. Paramount is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, and Veteran status.
 
 
 If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access https://www.paramount.com/careers as a result of your disability. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to paramountaccommodations@paramount.com. Only messages left for this purpose will be returned.
 
 
</div>

Machine Learning Engineer, Entry

Rubrik

<div><div><h1 class="jobSectionHeader">About the Team &amp; Role:</h1>
We're building SAGE, Rubrik's Semantic AI Governance Engine, which is the first system designed to monitor, govern, and remediate autonomous AI agents in real time. SAGE powers Rubrik Agent Cloud: enterprises define governance policies in natural language, and SAGE's custom small language models act as judges on every agent action. These models are fast enough to sit in the live request path and accurate enough that customers trust them with allow/block decisions on production traffic.
At its core, SAGE is &quot;LLM-as-judge&quot; applied to AI governance, utilizing the same technique most teams use for offline evaluation but productionized for real-time enforcement at enterprise scale. Our first-generation SLM Policy Guard already outperforms the larger frontier models we've benchmarked against on accuracy while running approximately 5x faster on the same workload. We're hiring to push that lead even further.
As an Applied ML Engineer on the SAGE team, you'll work end-to-end across the model lifecycle: curating data, training small models, serving them at production latency, and closing the feedback loop with real customer signals. The models you build don't just enforce policies in the live request path; they will also drive Agent Rewind, Rubrik's capability to instantly and precisely undo destructive autonomous-agent actions and restore the affected data to a trusted state.
We're a collaborative, applied team that ships models to enterprise customers within weeks, and we're passionate about proving that small, specialized models can outperform frontier LLMs at the problems that matter most for AI safety and governance.
<h2 class="jobSectionHeader">Nature of the Specialized Duties</h2>
<h3 class="jobSectionHeader">➢ Training, Fine-Tuning, and Distilling Production Small Language Models and Classifiers (25% of time)</h3>
<ul><li>Owning the full training lifecycle for the SLMs and classifiers in SAGE's real-time enforcement path, including base-model selection, supervised fine-tuning, preference optimization (DPO/RLAIF), and distillation from frontier teacher models.</li>
<li>Training anomaly and action-severity models that catch novel agent-side attack patterns at real-time decision latency, such as supply-chain compromises or emergent destructive behaviors not covered by any explicit policy. Severity scores route the highest-impact events to Agent Rewind for precise remediation.</li>
<li>Designing adversarial training pipelines like purpose-built adversarial agents and automated red-teams whose outputs feed directly into the next training run, turning every discovered weakness into a permanent model improvement.</li>
<li>Pushing the pareto frontier of accuracy, latency, and cost for governance-specific tasks through deliberate post-training choices (LoRA, quantization-aware training, distillation recipes, GRPO, etc.) and validating the wins on production traffic patterns.</li>
</ul><h3 class="jobSectionHeader">➢ Engineering High-Performance Model Serving and Inference Infrastructure (25% of time)</h3>
<ul><li>Designing multi-stage inference pipelines that handle both real-time enforcement (inline prompt, response, and tool-call blocking) and high-throughput batch workloads (offline scoring, back-testing, corpus mining) while processing billions of tokens daily across Global 2000 customer agent fleets.</li>
<li>Optimizing live deployments through shared GPU pools, KV-cache-aware routing, continuous batching, FP8/INT8 quantization, and speculative decoding to minimize inference cost while holding sub-second P99 SLOs.</li>
<li>Building serving-layer infrastructure that lets SAGE block agent prompts, responses, and tool calls in real time without becoming a latency bottleneck. This includes model gateway design, request routing, and graceful degradation.</li>
<li>Owning canary, shadow, and A/B traffic patterns so new model variants are validated against live customer traffic before they take enforcement decisions.</li>
</ul><h3 class="jobSectionHeader">➢ Building Synthetic Data Pipelines and Online + Offline Evaluation Frameworks (20% of time)</h3>
<ul><li>Designing automated data curation pipelines that mine live customer environments (with privacy and tenancy guarantees) for high-value per tenant training examples, such as long-tail violations, near-miss policy edges, or novel agent behaviors, and routing them back into the training loop for each customer.</li>
<li>Building automated policy back-testing by replaying historical agent traffic against new model and policy versions to catch regressions and recommend policy improvements before customer-visible deployment.</li>
<li>Building online evaluation systems for live model decisions, including shadow scoring, drift detection, calibration monitoring, and policy-coverage gap analysis, ensuring quality regressions surface in minutes rather than weeks.</li>
<li>Generating synthetic data using frontier teachers (adversarial prompts, policy-edge cases, multi-turn interactions) with evaluation that confirms synthetic data improves downstream quality, not just dataset size.</li>
</ul><h3 class="jobSectionHeader">➢ Insights Mining, Failure Diagnosis, and Adaptive Model Improvement (15% of time)</h3>
<ul><li>Building memory and context harnesses that fuse data sensitivity, identity, and historical agent behavior into real-time enforcement decisions to ensure SAGE reasons from each customer's specific context.</li>
<li>Mining agent insights across millions of sessions to surface security gaps, which are then turned into new policy proposals, refinements to existing policies, and signals about upstream issues across the agent ecosystem (Google ADK, Azure AI Foundry, Vertex AI, and others).</li>
<li>Building feedback loops that turn production decisions, customer-flagged false positives, and missed violations into one-click natural-language policy refinements to drive false-positive rates down without sacrificing recall.</li>
<li>Diagnosing model failures end-to-end and distinguishing data, training-recipe, architecture, and serving-layer root causes so fixes land in the right layer the first time.</li>
</ul><h3 class="jobSectionHeader">➢ Cross-Functional Collaboration and Translating Customer Reality into Modeling Problems (15% of time)</h3>
<ul><li>Providing technical leadership on a pillar of the SAGE model stack (training infrastructure, eval methodology, serving architecture, or insights pipeline), mentoring engineers ramping into ML, and shaping the team's technical roadmap.</li>
<li>Partnering with Product Management, customer-facing teams, and security analysts to translate customer agent-governance requirements into well-scoped modeling problems, and pushing back when ML is the wrong tool.</li>
<li>Communicating model behavior, tradeoffs, and limitations clearly to non-ML stakeholders, such as product managers and enterprise security leaders, so model decisions are made with full context.</li>
<li>Collaborating with Agent Cloud platform, security engineering, and AI research teams to integrate new SLMs into the real-time enforcement path with the right latency, observability, rollback, and tenancy guarantees.</li>
</ul><h2 class="jobSectionHeader">Minimum Requirements for the Position</h2>
Education: A Bachelor's degree (or higher) in Computer Science, Machine Learning, Computer Engineering, Statistics, or a closely related technical field is required. Designing production SLM training and serving systems requires a deep theoretical understanding of modern deep learning, optimization, and systems performance.
<h3 class="jobSectionHeader">Specialized Technical Knowledge:</h3>
<ul><li>2+ years of professional ML experience with demonstrable end-to-end production ownership; you have taken models from training to serving real customer traffic and stayed accountable for them through post-launch iteration.</li>
<li>Proficiency in Python and PyTorch (or equivalent) for production-grade training and evaluation.</li>
<li>Hands-on experience training, fine-tuning, or distilling language models or classifiers in a production setting, including SFT and at least one preference-optimization technique (DPO, RLAIF, or RLHF).</li>
<li>Production experience with serving frameworks (vLLM, SGLang, TensorRT-LLM, or equivalent), including optimization involving continuous batching, KV-cache strategy, and inference-time quantization.</li>
<li>Experience designing closed-loop ML systems, including the eval, telemetry, data-curation, and synthetic-data infrastructure that turns production signals back into training data and the next model release. You have built (not just used) at least one such loop.</li>
<li>Comfort operating at production scale, including debugging models that handle high QPS in safety-critical request paths where errors have customer-visible consequences.</li>
</ul><h3 class="jobSectionHeader">Preferred Qualifications:</h3>
<ul><li>Deep background in AI safety and red-teaming, including hands-on experience with adversarial ML, prompt injection defense strategies, and automated evaluation suites for enterprise-grade LLM safety.</li>
<li>Expertise in model evaluation methodology, specifically building &quot;LLM-as-judge&quot; pipelines, calibration monitoring, and adversarial benchmarks that surface the subtle failure modes static metrics often overlook.</li>
<li>Experience with context-fusion and retrieval systems that synthesize disparate signals - such as data sensitivity, user identity, and behavioral history - into high-fidelity model decisions.</li>
<li>Production experience with low-latency inference for streaming or safety-critical request paths where model throughput and P99 SLOs are paramount.</li>
<li>Mastery of label-efficient training and data mining, utilizing weak supervision, active learning, and embedding-based retrieval to surface the production examples that drive the most significant quality improvements.</li>
<li>Hands-on knowledge distillation experience, successfully transferring capabilities from frontier teacher models to specialized, small-scale student models for production serving.</li>
<li>Familiarity with the agentic ecosystem, including tool-use frameworks, model gateway architectures (MCP, LiteLLM, or equivalent), and autonomous agent patterns.</li>
<li>Active open-source contributions to mainstream ML training, serving, or evaluation libraries.</li>
</ul><div><div><div>The minimum and maximum base salaries for this role are posted below; additionally, the role is eligible for bonus potential, equity and benefits. The range displayed reflects the minimum and maximum target for new hire salaries for the role based on U.S. location. Within the range, the salary offered will be determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
</div><div>US Pay Range</div>
<div>$188,500&mdash;$282,700 USD
</div></div></div><div><h2 class="jobSectionHeader">Join Us in Securing and Accelerating the World's AI Transformation</h2>
Rubrik (RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real-time guardrails, fine-tuning for accuracy and undoing agentic mistakes. 

<h2 class="jobSectionHeader">Inclusion @ Rubrik</h2>
At Rubrik, we are dedicated to fostering a culture where people from all backgrounds are valued, feel they belong, and believe they can succeed. Our commitment to inclusion is at the heart of our mission to secure the world&rsquo;s data.
Our goal is to hire and promote the best talent, regardless of background. We continually review our hiring practices to ensure fairness and strive to create an environment where every employee has equal access to opportunities for growth and excellence. We believe in empowering everyone to bring their authentic selves to work and achieve their fullest potential.
<h3 class="jobSectionHeader">Our inclusion strategy focuses on three core areas of our business and culture:</h3>
<ul><li>Our Company: We are committed to building a merit-based organization that offers equal access to growth and success for all employees globally. Your potential is limitless here.
</li><li>Our Culture: We strive to create an inclusive atmosphere where individuals from all backgrounds feel a strong sense of belonging, can thrive, and do their best work. Your contributions help us innovate and break boundaries.
</li><li>Our Communities: We are dedicated to expanding our engagement with the communities we operate in, creating opportunities for underrepresented talent and driving greater innovation for our clients. Your impact extends beyond Rubrik, contributing to safer and stronger communities.
</li></ul><h2 class="jobSectionHeader">Equal Opportunity Employer/Veterans/Disabled</h2>
Rubrik is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Rubrik provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Rubrik complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please contact us at hr@rubrik.com if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.</div></div></div>

Mindrift

<div>
 
 Please submit your CV in English and indicate your level of English proficiency.
 
 
 
 Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.
 
 Participation is project-based, not permanent employment.
 
 
 
 
 What This Opportunity Involves
 
 
 
 While each project involves unique tasks, contributors may:
 
 
 <ul>
 <li>
 Design original computational STEM problems that simulate real scientific workflows
 </li>
 <li>
 Create problems that require Python programming to solve
 </li>
 <li>
 Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks)
 </li>
 <li>
 Develop problems requiring non-trivial reasoning chains and creative problem-solving approaches
 </li>
 <li>
 Verify solutions using Python with standard libraries (Numpy, Pandas, Scipy, scikit-learn)
 </li>
 <li>
 Document problem statements clearly and provide verified correct answers
 
 
 </li>
 </ul>
 
 What We Look For
 
 
 
 This opportunity is a good fit for ML specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:
 
 
 <ul>
 <li>
 5+ years of hands-on machine learning experience with proven business impact
 </li>
 <li>
 Portfolio of completed projects and publications showcasing real-world problem-solving
 </li>
 <li>
 Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels)
 </li>
 <li>
 Expert statistical analysis and machine learning - deep understanding of algorithms, methods, and their practical applications
 </li>
 <li>
 Expert with SQL and database operations for data manipulation and analysis
 </li>
 <li>
 Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
 </li>
 <li>
 Understanding of MLOps practices and model deployment workflows
 </li>
 <li>
 Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain)
 </li>
 <li>
 Strong written English (C1+)
 
 
 </li>
 </ul>
 
 How It Works
 
 
 
 Apply &rarr; Pass qualification(s) &rarr; Join a project &rarr; Complete tasks &rarr; Get paid
 
 
 
 Project time expectations
 
 
 
 For this project, tasks are estimated to require around 10-20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.
 
 
 
 Compensation
 
 
 
 On this project, contributors can earn up to
 
 $90 per hour equivalent
 
 , depending on their level and pace of contribution.
 
 
 Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.
</div>

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