{"id":856063,"url":"https://alion.io/job/openteams-senior-machine-learning-engineer-client-facing","title":"Senior Machine Learning Engineer - Client Facing","company":{"id":678978,"name":"OpenTeams","domain":"openteams.com","url":"https://alion.io/company/openteams","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":145000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"GCP","optional":true},{"name":"NumPy","optional":true},{"name":"SciPy","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"Travis CI","optional":true}],"status":"live","first_seen_at":"2026-06-30T21:01:30Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-09-27T23:46:25Z","board_verified":true,"closed_at":null,"days_open":89,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":89},"description":"Who We Are\nEvery organization runs on intelligence: years of accumulated knowledge, decisions, and context. As AI takes on more of that work, companies face a choice: rent that intelligence from vendors who keep the data, the context, and the results, or own it.\nOpenTeams exists to make ownership possible.\nFounded by Travis Oliphant, creator of NumPy and SciPy, and built by people with deep roots across the open-source ecosystem, including NumPy, SciPy, PyTorch, and Jupyter, we help enterprises and governments build AI they control, govern, and evolve themselves.\nIf that sounds like your kind of work, we'd like to meet you.\nJob Title: Senior Machine Learning Engineer\nLocation: Remote (U.S Strong Preference// W. Hemisphere Timezone required)\nWork Authorization: Authorized to work where they live\nSalary Range: 145,000 - 250,000 USD (dependent on experience level and location)\nAbout the Role\nWe're seeking a Machine Learning Engineer to join our team supporting a strategic client engagement focused on deep learning model development for customer behavior prediction. You'll work alongside client data scientists and engineers to enhance and optimize deep-learning models that drive business decisions at scale.\nThis role involves hands-on work across the ML lifecycle-from feature engineering to model architecture improvements-within a collaborative, research-informed environment. You'll have the opportunity to implement techniques from cutting-edge academic research while contributing to production systems that directly impact business outcomes.\nKey Responsibilities\nDevelop and refine features for deep learning models, working with large-scale customer and behavioral datasets\nImplement model architecture changes informed by recent academic research (e.g., papers from NeurIPS and similar venues)\nCollaborate with client teams to understand business context and translate requirements into technical solutions\nOptimize model training pipelines for efficiency and scalability\nDocument approaches, findings, and technical decisions for knowledge sharing across teams\nParticipate in code reviews and contribute to engineering best practices\nRequired Skills & Experience\nStrong proficiency with a deep learning framework (e.g. Pytorch, tensorflow)\nHands-on experience with feature engineering for predictive models\nSolid foundation in machine learning fundamentals (supervised learning, neural network architectures, optimization)\nAbility to read, understand, and implement techniques from ML research papers\nPython proficiency in a data science/ML context\nComfortable working in ambiguous environments and adapting to unfamiliar tooling\nNice to Have\nExperience with time-series or sequential modeling\nMLOps experience (model deployment, monitoring, pipeline orchestration)\nFamiliarity with Google Cloud Platform or large-scale distributed training\nBackground in causal inference or attribution modeling\nExperience working in consulting or client-facing technical role\nGrow With Us\nAt OpenTeams, growth isn’t just about the company-it’s about you.\nWe believe the best careers are built at the edge of your potential. That is where new tools, ideas, and technologies change the world. Here, you’ll work alongside pioneers of AI, solving problems that matter: making AI more transparent, more ethical, and more empowering. As your skills grow, our career framework provides a pathway and recognition of that increased impact.\nOpportunities aren’t limited by geography. You’ll collaborate with global experts, contribute to open source projects that power the world’s technology, and stretch your skills daily. That global perspective and diversity makes our solution more universal and robust. We are committed to continuing to celebrate diversity on our team.\nSupported people are successful people. We offer 100% employer paid medical premiums for employees and self-managed PTO with a minimum time off requirement, so that our teams are able to do their best work.\nWe invest in curiosity, creativity, and ownership. That means you’ll be trusted to boldly innovate, supported to learn fast, and celebrated for successful collaboration.\nCommitment to diversity, equity, inclusion, and belonging\nOpenTeams understands that valuing diverse creative practices and forms of knowledge is crucial to and enriches the company’s core mission. We encourage applications from everyone, including members of all equity-seeking communities, such as (but certainly not limited to) women, racialized and Indigenous persons, disabled people, persons of all sexual orientations, gender identities and expressions.\nWe are an equal opportunity employer - all qualified applicants will receive equal consideration for recruitment, interviews, employment, training, compensation, promotion, and related activities. We do not discriminate based on race, religion, gender, gender identity, gender expression, color, national origin, pregnancy, ancestry, domestic partner status, disability, sexual orientation, age, genetic predisposition, medical condition, marital status, citizenship status, military or veteran status, or any other basis covered by applicable laws. OpenTeams will not tolerate discrimination or harassment based on these characteristics or any other unlawful behavior, conduct, or purpose.","description_format":"text","description_chars":5304,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Consulting & Integration","Open Source Projects & Foundations"],"lifecycle":[{"event":"open","at":"2026-09-13T06:44:15Z"}],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.268,"p_room":0.28,"age_days":88,"expected_fill_days":24,"reasons":["conf:0","velocity","win:tail","crowd:"],"computed_at":"2026-09-27T05:45:00Z"},"pay":{"stated_usd_annual":250000,"is_top_pay":true},"html_url":"https://alion.io/job/openteams-senior-machine-learning-engineer-client-facing","json_url":"https://alion.io/job/openteams-senior-machine-learning-engineer-client-facing.json","meta":{"generated_at":"2026-09-28T02:51:25Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1667,"day_limit":5000,"remaining_today":3333,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}